USING DEEP RNA SEQUENCING DATA TO IDENTIFY ANTIMICROBIAL-RESISTANT BACTERIA

Description

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with government support under P20 GM121344, R35 GM118097, R01 GM127472, and R35 GM142638 awarded by the National Institutes of Health. The government has certain rights in the invention.

TECHNICAL FIELD OF THE INVENTION

This invention generally relates to the chemical analysis of biological material, using nucleic acid products used in the analysis of nucleic acids, e.g., primers or probes for diseases caused by alterations of genetic material.

SEQUENCE LISTING

The instant application contains a Sequence Listing which has been submitted via Patent Center and is hereby incorporated by reference in its entirety. Said .xml copy, created on May 29, 2025, is named 405002-539001 US, and is 20,188 bytes in size.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent application claims priority under 35 U.S.C. § 120 to U.S. Ser. No. 18/528,750, filed on Dec. 4, 2023, published on Aug. 8, 2024, as U.S. Pat. Publ. 2024/0263252, which claims priority under 35 U.S.C. § 119(e) to the provisional patent applications U.S. Ser. No. 63/378,365 and U.S. Ser. No. 63/378,366, both filed Oct. 4, 2022. This patent application also claims priority under 35 U.S.C. § 119(e) to provisional patent application U.S. Ser. No. 63/699,049.

BACKGROUND OF THE INVENTION

The following presents a simplified summary of the innovation to provide a basic understanding of some aspects of the invention. This summary is not an extensive overview of the invention. This sum is intended to neither identify key or critical elements of the invention nor delineate the scope of the invention. The sole purpose is to present some concepts of the invention in a simplified form as a prelude to the more detailed description that is presented later.

Antimicrobial-resistant bacteria cause almost five million deaths each year. Murray et al., Lancet, 309 (1035), 629-655 (Feb. 12, 2022). Early identification of the causative pathogen and its antibiotic resistance pattern is central to infection management by focusing on antimicrobial administration. Typical clinical practice in patients with infections would benefit by starting treatment with broad-spectrum antibiotics as early as possible.

Despite the benefit of broad-spectrum antibiotics in reducing infection mortality, there are negative consequences. Broad-spectrum antibiotics are costly and labor-intensive. They increase the risk of Clostridioides difficile colitis and select new, antibiotic-resistant pathogens.

Culturing the site of infection identifies the pathogen and its associated antibiotic resistance but can take days to generate actionable information. Antibiotics administered before sample acquisition can reduce culture yields.

There is a need in the biomedical art for diagnostic tests for diagnosing and treating sepsis.

SUMMARY OF THE INVENTION

The invention provides molecular diagnostic tests to overcome the limitations of conventional microbiological approaches for diagnosing and treating sepsis.

In a first embodiment and as a proof of principle, the inventors develop polymerase chain reaction (PCR) diagnostic tests for four common bacteria, Staphylococcus aureus, extra-intestinal Escherichia coli, Pseudomonas æruginosa, and Hæmophilus influenzae.

In a second embodiment, the inventors create tests of clinically relevant resistance genes associated with these four pathogens. These bacteria are pathogens of interest in the request for applications (RFA-AI-22-010) for bacteremia (Staphylococcus aureus, Escherichia coli, Pseudomonas æruginosa) and pneumonia (Staphylococcus aureus, Pseudomonas æruginosa, Hæmophilus influenzae). These pathogens are the most common causes of bacteremia and hospital-acquired pneumonia at Rhode Island Hospital. These four pathogens have antimicrobial resistance attributable to specific genes requiring antibiotic management changes. These four pathogens are the top organisms that cause death due to resistance. Murray et al., Lancet, 309 (1035), 629-655 (Feb. 12, 2022).

In a third embodiment, the invention provides tests of clinically relevant resistance genes associated with other pathogens that cause sepsis.

In a fourth embodiment, the invention provides a diagnostic PCR test based on bacterial RNA. PCR tests were developed for respiratory pathogens. See Covert, Bashore, Edds, & Lewis (2021). PCR tests were developed for identifying the DNA of bacteria like Staphylococcus aureus in targeted sites. Palavecino (2020). Pathogen identification was previously done by sequencing cell-free DNA from blood. Camargo et al. (2019).

In the diagnostic PCR test, the most abundant RNA targets are selected from the blood of patients with these infections, making the approach more sensitive than single-copy DNA targets. Antibiotic resistance correlates with gene expression. The risk of RNA degradation is mitigated by stabilizing RNA. Because the targets are derived from RNA sequencing data, those RNAs are abundant and measurable in patients with infection.

In one aspect, the unmapped RNA reads from patients with infections that align with pathogens can inform a better diagnostic test. PCR targets for diagnostics come from a data set created from deep sequencing (>100 million reads) of the blood of patients with bacteremia or pneumonia. Pathogen identification is performed with standard culture techniques. The RNA sequences from the pathogens are discarded in transcription analysis because they would not align with the human genome. These unmapped reads are being identified in the blood of patients and aligned to a custom genome derived from them pathogens of interest to identify the causative organism. RNAs that align with resistance genes are identified.

In a fifth embodiment (A1a), the invention provides a direct from blood, without culture, reverse transcriptase polymerase chain reaction (RT-qPCR) test for bacteria causing bacteremia, specifically Staphylococcus aureus, Escherichia coli, Pseudomonas æruginosa, based on the RNA identified in patients with bacteremia caused by these organisms.

In a sixth embodiment (A1b), the invention provides a method to validate these RT-qPCR tests in samples from patients with and without bacteremia.

In a seventh embodiment (A2a), the invention provides a direct from blood, without culture, reverse transcriptase polymerase chain reaction (RT-qPCR) test for bacteria causing pneumonia, specifically Staphylococcus aureus, Pseudomonas æruginosa, and Hæmophilus influenzae, based on the RNA identified in patients with pneumonia caused by these organisms.

In an eighth embodiment (A2b), the invention provides a method to validate these RT-qPCR tests in samples from patients with and without pneumonia.

In a ninth embodiment (A3a), using the RNA from patients with infections, the invention provides an RT-PCR for the most common resistance genes that would influence treatment for Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, and Hæmophilus influenzae.

In a tenth embodiment (A3b), the invention provides a method to validate these PCR tests for resistance genes in samples from patients with and without infections.

In an eleventh embodiment, the invention provides a direct from blood RT-PCR test for bacteremia caused by Staphylococcus aureus, Escherichia coli, and Pseudomonas æruginosa without culture with phenotypic microbial resistance identification.

In a twelfth embodiment, the invention provides a direct from blood RT-PCR test for pneumonia caused by Staphylococcus aureus, Pseudomonas æruginosa, and Hæmophilus influenzae without culture with phenotypic microbial resistance identification.

In a thirteenth embodiment, the invention provides that all tests can have a result in fewer than four hours from the time of sample collection.

In a fourteenth embodiment, the invention provides the ability to standardize and scale these tests for a clinical microbiology setting. See TABLE 6 below.

In another aspect, the invention provides a direct form blood PCR panel, e.g., using the top twelve pathogens, that identifies the pathogens and resistance profile faster (fewer than four hours) than current bacteremia and hospital-acquired pneumonia techniques. This invention translates deep RNA sequencing data into a product, a rapid PCR to identify Staphylococcus aureus, Escherichia coli, Pseudomonas æruginosa, and Hæmophilus influenzae and potential resistance genes without culture or specimens other than blood.

Several factors provided improvements to RNA sequencing that can make RNA sequencing useful for diagnostic and therapeutic methods of treating sepsis using a standard set of testing conditions. These improvements are supported by the literature and preliminary data indicating that RNA sequencing can identify bacterial pathogens directly from the blood of patients with those infections.

An Illumina Machine (NovaSeq X+) should be able to take only thirteen hours to obtain 1.6 billion reads, not including processing time.

The commonly identified and organism-specific sequences (for Staphylococcus aureus, Escherichia coli, Pseudomonas æruginosa, and Hæmophilus influenzae) are the template for designing oligonucleotide primers for RT-qPCR tests. Future efforts to expand the diagnostic test to other pathogens may require RNA sequencing from patients with pneumonia due to other pathogens.

Direct RNA sequencing should be done for these assays. This conversion of RNA to DNA takes time. Direct RNA sequencing allows faster processing times, to be completed in the 4-hour time frame. Focusing on RNA rather than DNA improves phenotypic correlation with antimicrobial resistance.

RNA sequencing can be a valuable tool in personalizing the care of sepsis patients. With these advances, this tool will be used by clinicians in the Intensive Care Unit caring for sepsis patients. The improvements described above should expand the technology from the research laboratory to the clinical microbiology laboratory.

The improvements listed enable a direct from blood, without culture, reverse transcriptase polymerase chain reaction (RT-qPCR) test for bacteria and methods to validate these RT-qPCR tests in samples from patients. RT-qPCR tests are useful in the diagnosis and treatment of sepsis. The improvements listed above enable methods for combatting the scourge of drug-resistant bacteria.

The improvements listed are useful for designing better platforms and reagents for direct from blood, without culture, reverse transcriptase polymerase chain reaction (RT-qPCR) tests for bacteria. QIAGEN (Germantown, MD, USA) is a manufacturer of platforms and reagents for RNA isolation and sequencing. Abbott (Abbott Park, IL, USA), Cepheid (Sunnyvale, CA, USA), Thermo Fisher Scientific (Waltham, MA, USA), and ELITech Group (Puteaux, FR) are manufacturer of platforms and reagents for RNA isolation and sequencing.

RNA sequencing can be a medically useful tool for personalizing the care of sepsis patients. With these advances, this tool will be used by clinicians in the Intensive Care Unit caring for sepsis patients.

The technology advantageously provides the following features, which can be inter-combined with any other embodiment disclosed herein.

The fifteenth embodiment is a method for diagnosing infections and antibiotic resistance, comprising performing RNA sequencing on a blood sample from a patient with sepsis; identifying pathogen RNA targets and resistance genes from the RNA sequencing data that do not map to the human genome; designing PCR primers based on the identified pathogen RNA targets and resistance genes; and using the PCR primers in a PCR test to diagnose the infection and determine antibiotic resistance in the patient.

The sixteenth embodiment is a method of the fifteenth embodiment, wherein the RNA sequencing is performed on peripheral blood from the patient.

The seventeenth embodiment is the method of the fifteenth embodiment, wherein the PCR test diagnoses the infection and determines antibiotic resistance faster than culture-based methods.

The eighteenth embodiment is the method of the fifteenth embodiment, further comprising aligning the RNA sequencing data to resistance gene and pathogen genome databases to identify the pathogen RNA targets and resistance genes.

The nineteenth embodiment is the method of the fifteenth embodiment, wherein the PCR primers are designed to specifically identify the pathogen causing the infection and any antibiotic resistance genes present.

The twentieth embodiment is the method of the fifteenth embodiment, wherein the PCR test provides a rapid diagnosis of the infection and antibiotic resistance to guide treatment decisions.

The twenty-first embodiment is the method of the fifteenth embodiment, wherein the method allows for limiting the use of broad-spectrum antibiotics by providing a targeted diagnosis.

The twenty-second embodiment is the method of the fifteenth embodiment, wherein the patient is diagnosed with sepsis before performing the RNA sequencing.

The twenty-third embodiment is the method of the fifteenth embodiment, wherein the blood sample is obtained within twenty-four hours of the patient being diagnosed with sepsis.

The twenty-fourth embodiment the method of the fifteenth embodiment, wherein the PCR test is performed within sixth hours of obtaining the blood sample.

The twenty-fifth embodiment is a system for diagnosing infections and antibiotic resistance, comprising: an RNA sequencing apparatus configured to perform RNA sequencing on a blood sample from a patient with sepsis; a computing device configured to identify pathogen RNA targets and resistance genes from the RNA sequencing data that do not map to the human genome; and design PCR primers based on the identified pathogen RNA targets and resistance genes; and a PCR apparatus configured to use the PCR primers in a PCR test to diagnose the infection and determine antibiotic resistance in the patient.

The twenty-sixth embodiment is the system of the twenty-fifth embodiment, wherein the RNA sequencing apparatus is configured to perform the RNA sequencing on peripheral blood from the patient.

The twenty-seventh embodiment is the system of the twenty-fifth embodiment, wherein the PCR apparatus is configured to diagnose the infection and determine antibiotic resistance faster than culture-based methods.

The twenty-eighth embodiment is the system of the twenty-fifth embodiment, wherein the computing device is further configured to align the RNA sequencing data to resistance gene and pathogen genome databases to identify the pathogen RNA targets and resistance genes.

The twenty-ninth embodiment is the system of the twenty-fifth embodiment, wherein the computing device is configured to design the PCR primers to specifically identify the pathogen causing the infection and any antibiotic resistance genes present.

The thirtieth embodiment is the system of the twenty-fifth embodiment, wherein the PCR test performed by the PCR apparatus provides a rapid diagnosis of the infection and antibiotic resistance to guide treatment decisions.

The thirty-first embodiment is the system of the twenty-fifth embodiment, wherein the system allows for limiting the use of broad-spectrum antibiotics by providing a targeted diagnosis.

The thirty-second embodiment is the system of the twenty-fifth embodiment, wherein the blood sample is obtained from a patient diagnosed with sepsis.

The thirty-third embodiment is the system of the twenty-fifth embodiment, wherein the blood sample is obtained within twenty-four hours of the patient being diagnosed with sepsis.

The thirty-fourth embodiment is the system of the twenty-fifth embodiment, wherein the PCR apparatus is configured to perform the PCR test within six hours of the blood sample being obtained.

The thirty-fifth embodiment is a method for identifying pathogens and their antimicrobial resistance genes in a subject suspected of having an infectious disease, comprising obtaining a peripheral blood sample from the subject; extracting RNA from the peripheral blood sample; performing high-throughput RNA sequencing on the extracted RNA to generate RNA sequencing data; filtering the RNA sequencing data to remove sequences aligning to the human genome; aligning the filtered RNA sequencing data to one or more curated antimicrobial resistance gene databases and one or more curated pathogen genome databases; designing specific molecular primers based on the aligned RNA sequencing data, wherein the molecular primers target sequences specific to the one or more pathogenic microorganisms and the one or more antimicrobial resistance genes; and identifying, using the specific molecular primers in a polymerase chain reaction (PCR) assay, the presence and identity of one or more pathogenic microorganisms and one or more antimicrobial resistance genes associated with the one or more pathogenic microorganisms in the peripheral blood sample.

The thirty-sixth embodiment is the method of the thirty-fifth embodiment, wherein the subject is a patient diagnosed with sepsis or suspected of having sepsis based on clinical signs and symptoms.

The thirty-seventh embodiment is the method of the thirty-fifth embodiment, wherein the subject is a patient with a confirmed infectious disease or suspected of having an infectious disease based on clinical signs and symptoms.

The thirty-eighth embodiment is the method of the thirty-fifth embodiment, wherein the RNA sequencing data that does not align to the human genome is further filtered to remove low-quality sequences and contaminant sequences before aligning to the one or more antimicrobial resistance gene databases and the one or more pathogen genome databases.

The thirty-ninth embodiment is the method of the thirty-fifth embodiment, wherein the specific molecular primers are used in a quantitative real-time PCR (qRT-PCR) assay to simultaneously identify and quantify the one or more pathogenic microorganisms and the one or more antimicrobial resistance genes in the peripheral blood sample.

The fortieth embodiment is the method of the thirty-fifth embodiment, wherein the PCR assay is performed using a multiplex PCR platform capable of detecting multiple targets in a single reaction.

The forty-first embodiment the method of the thirty-fifth embodiment, wherein the method is performed by a trained healthcare professional in a clinical laboratory setting equipped with high-throughput sequencing and PCR facilities.

The forty-second embodiment is the method of the thirty-fifth embodiment, wherein the method can diagnose the presence and identity of the one or more pathogenic microorganisms and the one or more antimicrobial resistance genes within twenty-four hours of obtaining the peripheral blood sample, which is faster than standard culture-based diagnostic methods that typically require 48-72 hours.

The forty-third embodiment is the method of the forty-second embodiment 2, wherein rapidly identifying the presence and identity of the one or more pathogenic microorganisms and the one or more antimicrobial resistance genes in the patient with sepsis enables timely and appropriate selection of targeted antimicrobial therapy, which improves clinical outcomes and reduces mortality in sepsis.

The forty-fourth embodiment is the method of the thirty-fifth embodiment, wherein identifying the specific one or more antimicrobial resistance genes associated with the one or more pathogenic microorganisms enables the selection of effective and targeted antimicrobial treatments, while avoiding the unnecessary use of broad-spectrum antibiotics, thereby facilitating antimicrobial stewardship and reducing the spread of antimicrobial resistance.

The forty-fifth embodiment is a method for diagnosing and treating infectious diseases in a subject, comprising obtaining a biological sample from the subject; extracting RNA from the biological sample; performing high-throughput RNA sequencing on the extracted RNA to obtain RNA sequencing data; identifying, based on the RNA sequencing data, one or more pathogenic microorganisms and one or more antimicrobial resistance genes associated with the one or more pathogenic microorganisms by aligning the RNA sequencing data to one or more curated antimicrobial resistance gene databases and one or more curated pathogen genome databases; and designing specific molecular primers based on the aligned RNA sequencing data, wherein the molecular primers target sequences specific to the one or more pathogenic microorganisms and the one or more antimicrobial resistance genes; determining an antimicrobial susceptibility profile of the one or more pathogenic microorganisms based on the identified one or more antimicrobial resistance genes; and selecting a targeted antimicrobial treatment for the subject based on the determined antimicrobial susceptibility profile.

The forty-sixth embodiment is the method of the forty-fifth embodiment, wherein the biological sample is peripheral blood, and the RNA sequencing data is obtained by performing high-throughput RNA sequencing on RNA extracted from the peripheral blood sample.

The forty-seventh embodiment is the method of the forty-fifth embodiment, wherein identifying the one or more pathogenic microorganisms and the one or more antimicrobial resistance genes further comprises filtering the RNA sequencing data to remove sequences aligning to the human genome and other contaminant sequences; and using the specific molecular primers in a quantitative real-time PCR (qRT-PCR) assay to confirm the presence and identity of the one or more pathogenic microorganisms and the one or more antimicrobial resistance genes in the biological sample.

The forty-eighth embodiment is the method of the forty-fifth embodiment, wherein the targeted antimicrobial treatment is selected to specifically target the identified one or more pathogenic microorganisms while minimizing the use of broad-spectrum antibiotics, based on the determined antimicrobial susceptibility profile.

The forty-ninth embodiment is the method of the forty-fifth embodiment, wherein the method is performed by a multidisciplinary team of healthcare professionals, including clinicians, microbiologists, and bioinformaticians, in a hospital setting equipped with high-throughput sequencing and PCR facilities.

The fiftieth embodiment is the method of the forty-fifth embodiment, wherein the method can diagnose the infectious disease and determine the appropriate targeted antimicrobial treatment within 24-48 hours of obtaining the biological sample, which is faster than standard culture-based diagnostic methods that typically require 48-72 hours for pathogen identification and antimicrobial susceptibility testing.

The fifty-first embodiment is the method of the forty-fifth embodiment, wherein the infectious disease is sepsis, and the rapid diagnosis and targeted antimicrobial treatment enabled by the method improves clinical outcomes and reduces mortality in patients with sepsis compared to standard care.

The fifty-second embodiment is the method of feature 33, wherein the qRT-PCR assay is performed using a multiplex PCR platform capable of simultaneously detecting and quantifying multiple pathogenic microorganisms and antimicrobial resistance genes in a single reaction, thereby increasing the efficiency and accuracy of the diagnostic process.

The fifty-third embodiment is the method of feature 34, wherein selecting the targeted antimicrobial treatment based on the determined antimicrobial susceptibility profile facilitates antimicrobial stewardship by ensuring the appropriate use of antibiotics and reducing the unnecessary use of broad-spectrum antibiotics, thereby minimizing the spread of antimicrobial resistance.

The fifty-fourth embodiment is the method of the forty-fifth embodiment, wherein the biological sample comprises one or more of peripheral blood, sputum, bronchoalveolar lavage fluid, cerebrospinal fluid, urine, wound swabs, or tissue biopsies, depending on the suspected site of infection and the clinical presentation of the subject.

In yet other embodiments, a method of making and/or training a system, machine learning method, or artificial intelligence (AI) method is provided comprising a system or method discussed in any of the embodiments above and optionally adding one or more acceptable datasets. The technology disclosed herein is surprisingly effective and solves many of the larger problems discussed herein.

Other implementations are described and recited herein. These and other features and advantages will be apparent from a reading of the following detailed description and a review of the associated drawings. Both the foregoing general description and the following detailed description are explanatory only and are not restrictive of aspects as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

For illustration, some embodiments of the invention are shown in the drawings described below. Like numerals in the drawings indicate like elements throughout. The invention is not limited to the precise arrangements, dimensions, and instruments shown.

FIG. 1 shows the RT-qPCR validation of sequencing results. cDNA from patient RNA was tested for the SARS-CoV-2 N gene using real-time PCR. FIG. 1A is a map of the N gene showing the location of the peak of sequencing reads (red box) and primers directed to the peak or elsewhere in the gene (off peak). FIG. 1B is a bar graph showing the relative expression level of peak and off-peak sequences in fifteen COVID-19 patients. The peak sequence is shown in red and off-peak in black.

FIG. 2A is a schematic showing the structure of a SARS-CoV2-Nucleocapsid (N2) gene. FIG. 2B is a bar chart showing. FIG. 2C is a bar chart showing.

FIG. 3 is a schematic showing significant differences of the minimum free energy of RNA-Seq reads in different genes. Lines between the N gene and ORF1ab, between N gene and ORF6, and between ORF6 and the S gene represent the significant differences between genes.

FIG. 4 is a schematic showing significant differences of the ensemble free energy of RNA-Seq reads in different genes. Lines between the N gene and ORF1 ab, between N gene and ORF6, between ORF6 and the S gene, and between ORF6 and ORF3a represent the significant differences between genes.

FIGS. 5A-5C are a set of bar graphs showing examples of motif effects on energy. FIG. 5A shows the results for minimum free energy (left) and ensample free energy (right) for samples that have the motif MEME-ChIP 9 compared with samples that do not. The MEME-ChIP 9 motif may increase the minimum free energy (see left). This motif may destabilize RNA sequence reads and may increase the ensemble free energy (see right). FIG. 5B shows the results for minimum free energy (left) and ensample free energy (right) for samples that have the motif MEME-28 compared with samples that do not. The MEME-28 motif may decrease the minimum free energy (see left). This motif may stabilize RNA sequence reads (see right) and decrease the ensemble free energy. FIG. 5C shows the results for minimum free energy (left) and ensample free energy (right) for samples that have the motif MEME-15 compared with samples that do not. The MEME-15 motif does not seem to affect the minimum free energy (see left). This motif may not affect RNA sequence read stability (see right) and does not seem to affect the ensemble free energy.

FIG. 6 (SEQ ID NO.: 7-15) is a set of calculated sequence results from MEME-ChIP analysis. Only ten results were requested to be given with three significantly affecting the minimum free energy and ensemble free energy of read sequences.

FIGS. 7A-7I is a set of exemplary secondary structures of reads based on minimum free energy with discovered motifs. FIG. 7A (SEQ ID NO.: 16) shows MEME-ChIP 8. FIG. 7B (SEQ ID NO.: 16) shows MEME-ChIP 9. FIG. 7C (SEQ ID NO.: 17) shows MEME-ChIP 10. FIG. 7D (SEQ ID NO.: 18) shows MEME-9. FIG. 7E (SEQ ID NO.: 17) shows MEME-10. FIG. 7F (SEQ ID NO.: 18) shows MEME-21. FIG. 7G (SEQ ID NO.: 19) shows MEME-22. FIG. 7H (SEQ ID NO.: 20) shows MEME-27. FIG. 7I (SEQ ID NO.: 21) shows MEME-28.

FIG. 8 is a chart of density plots used to identify the areas of the genomes with the most reads.

FIG. 9 is a pair of reads from two groups of patients with positive blood cultures compared to patients that were found to not have an Escherichia coli infection.

FIG. 10 shows an example workflow for the development and testing of PCR primers for pathogens and resistance from RNA sequencing data of sepsis patients.

TABLE 1 lists the baseline characteristics of ICU patients with sepsis with RNA sequencing data.

TABLE 2 lists the patients with positive blood cultures and number of unmapped reads that align to each pathogen.

TABLE 3 shows the cohort of patients that PCRs were tested on with clinical data and relevant PCR results.

eTABLE 1 is a list of pathogens and the genome used to align unmapped reads.

eTABLE 2 is a list of resistance genes used to align unmapped reads.

eTABLE 3 is a list of patients and number of unmapped reads that align to each pathogen.

eTABLE 4 is a list of Escherichia coli and Staphylococcus aureus primers (eTABLE 4A) with targets (eTABLE 4B)

eTABLE 5 is a list of Pseudomonas æruginosa targets with primers.

eTABLE 6 is a list of Staphylococcus aureus targets with primers.

eTABLE 7 is a list of resistance gene targets with primers

eTABLE 8 is a list of PCR results with cycle counts and controls. All experiments done in duplicate and results averaged. If the cell is blank, there were more than 40 cycle counts. RT=reverse transcriptase, NRT=no reverse transcriptase. Beta actin was done as control. Targets are grouped by pathogen then resistance genes.

All tables and eTABLES included herein are further described below.

All trademarks, images, likenesses, words, and depictions in the drawings and the disclosure are in fair use and are provided solely for the purposes of illustration of the invention in view of an urgent need to treat subjects as further discussed in detail below.

DETAILED DESCRIPTION OF THE INVENTION

The subject innovation is now described in some instances, when necessary, with reference to the drawings. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present invention. It may be evident that the present invention may be practiced without these specific details. In other instances, well-known structures, methods, and devices are shown in block diagram form or with illustrations in order to facilitate describing the present invention. Certain aspects, modes, embodiments, variations and features of the invention are described below in various levels of detail in order to provide a substantial understanding of the present invention.

INDUSTRIAL APPLICABILITY

Faster pathogen identification for severe infections. Sepsis causes one out of five deaths in the world. Rudd et al., Lancet (London, England), 395(10219), 200-11 (2020). The diagnosis of septic infection is a significant challenge for sepsis care. Duncan, Youngstein, Kirrane, & Lonsdale (2021). This invention provides better diagnoses of bacterial infections and associated antimicrobial resistance to improve outcomes.

The Surviving Sepsis Campaign standardized treatment for sepsis that includes blood cultures before broad-spectrum antibiotics and start of antibiotics within one hour. See Evans et al., Critical Care Medicine, 49(11), e1063-e1143 (Nov. 1, 2021). In a multivariate analysis of factors affecting mortality in patients with septic shock, the time to begin antibiotic treatment was the most impactful variable. Kumar et al. showed this impact, reporting a 79.9% survival in septic shock patients with antibiotics in the first hour and a reduction of 7.6% for every hour delay. Kumar et al., Critical Care Medicine, 34(6), 1589-96 (June 2006). Vazquez-Guillamet et al. determined that the number needed to treat with antibiotics to save one life was five. Vazquez-Guillamet et al., (2014). Faster pathogen identification improves sepsis outcomes by guiding antibiotic selection. Current methods take too much time, such as days. Sepsis kills in hours.

This invention provides diagnostic tests for three pathogens that cause bacteremia and three pathogens that cause pneumonia to shorten the time to pathogen-specific treatment for diseases such as sepsis.

Antimicrobial resistance is a significant health problem. Across the world, antimicrobial-resistant bacteria cause almost five million deaths each year. Murray et al., Lancet, 309 (1035), 629-655 (Feb. 12, 2022). Antimicrobial-resistant bacteria cause over 100,000 deaths in the United States with costs of over $21 billion. With each antibiotic, resistance follows shortly after that. See Clatworthy, Pierson, & Hung (2007). The United Nations convened a high-level meeting on Antimicrobial Resistance on Sep. 21, 2016, that included statements by global leaders such as Secretary-General Ban Ki-moon: “Drug resistance imposes huge costs on health systems and is taking a growing—and unnecessary—toll in lives and threatening to roll back much of the progress we have made.” Locally the trend is increased in resistance increasing among many pathogens. Kassakian & Mermel, Antimicrobial Resistance and Infection Control, 3(1), 9 (2014).

Importance of phenotypic antibacterial susceptibility. Bacteria have multiple antimicrobial resistance mechanisms and are easily transferred, resulting in pathogens with extensive resistance profiles. Harbottle, Thakur, Zhao, & White (2006). Despite advances in genomic testing, resistance genes found in DNA do not always correlate with phenotypic resistance. Bortolaia et al., The Journal of Antimicrobial Chemotherapy, 75(12), 3491-500 (2020). Some computational approaches were suggested to handle this data. Bortolaia et al., The Journal of Antimicrobial Chemotherapy, 75(12), 3491-500 (2020). Reasons for the lack of correlation between genomic data and resistance phenotypes include lack of transcription and DNA not associated with a living cell. Using RNA sequencing data allows for a better correlation between the genomic data and the expression of resistance. Using RNA data identifies only genes actively being transcribed, thus measuring gene expression levels.

Facilitate antimicrobial stewardship. Antibiotic stewardship was suggested as a method to combat resistance. Broad-spectrum antibiotics, although appropriate, have an adjusted increased mortality risk. Webb et al., The European Respiratory Journal, 54(1) (2019). Other studies specifically state that broad-spectrum antibiotics increase the risk of in-hospital death when no resistance is identified. Rhee et al., JAMA Network Open, 3(4), e202899 (Apr. 1, 2020). Broad-spectrum coverage is used in 67.8% of patients. Rhee et al., JAMA Network Open, 3(4), e202899 (Apr. 1, 2020). De-escalation is important because new resistance emerges each day of inappropriate antibiotic exposure. Teshome et al. (2019). In the hospital setting, empiric therapy is only de-escalated 16% of the time. De Bus et al. (2020). Costs are reduced when de-escalation is used. Seok, Jeon, & Park (2020). Narrowing antibiotics reduces labor in the ICU. Mei-Sheng, Riley & Olans (2021). A diagnostic test that rapidly identifies a pathogen and its antimicrobial resistance should help in antimicrobial stewardship.

In the twelfth embodiment above, PCRs informed by a large dataset are performed in four hours, yielding direct from blood results independent of culture. Al-Hasan, Winders, Bookstaver, & Justo recommend directly assessing stewardship programs rather than looking for adverse events. AI-Hasan, Winders, Bookstaver, & Justo (2019). With faster bacteria identification, serial testing could assess treatment efficacy. Serial testing could be an additional metric in stewardship programs as antibiotics could be stopped sooner.

Unmapped reads identify bacterial RNA with deep RNA sequencing. In the initial assessment of RNA sequencing data, the reads are aligned to the genome of the species the sample came from, commonly the human genome. Unmapped reads can account for up to 20% of the data. These data are typically discarded. In the samples of humans with the illness, there are more unmapped reads (˜35%). Monaghan et al. (2021). The Read Origin Protocol (ROP) (Mangul et al. (2018)) and Kraken (Wood, Lu, & Langmead (2018)) have been developed to determine the origin of unmapped reads. The Read Origin Protocol analysis of multiple data sets mapped 99.9% of all reads. The data typically discarded were analyzed in a seven-step process. One step is of particular interest because of the relevance to the patient population in this work: bacterial reads. Using ROP, or more recently Kraken2, bacterial RNA was identified in the blood samples of patients with sepsis, which mapped to the bacteria found in blood culture. RNA sequencing data can inform primer design to produce better diagnostic tests.

Diagnostic solution. This invention leverages a large data set of unmapped reads from patients diagnosed with infections by the gold standard of bacterial culture. This deep RNA sequencing data suggests PCR primers to identify pathogens, such as Staphylococcus aureus, Escherichia coli, Pseudomonas æruginosa, and Hæmophilus influenzae, and clinically relevant resistance genes. These tests are culture-independent and allow for direct from blood testing where PAXgene tubes stabilize the RNA. The PAXgene Blood RNA Tubes (QIAGEN, MD, USA; Cat. No./ID: 762165) are used for in vitro diagnostic testing (IVD). Sensitivity and specificity align with the requirements of the FDA as it relates to molecular diagnostic tests. Because RNA is used, phenotypic identification is done better than attempts at DNA sequencing. See BMJ Global Health, 5(11) (2020).

Conceptual innovation. Reads that do not align to the genome of interest (human in these assays) are typically discarded. In this invention, the unmapped reads are the focus of the investigation to identify PCR targets in patients with bacterial infections. Deep RNA sequencing, greater than 100 million reads, allows for identifying bacterial RNA in the blood of patients with infections. Focusing on RNA rather than DNA improves phenotypic correlation with antimicrobial resistance. Globin and ribosomal RNA are reduced to enhance the identification of the bacterial genes expressed. Clinical management is guided by these RNA-based PCR tests designed to identify target genes directly affecting treatment decisions. The RNA-based PCR tests are developed with the goal of rapid dissemination to clinical microbiology laboratories.

Technical innovation. Unmapped reads from deep RNA sequencing are an untapped resource of new information. Typically, 30% of reads are unmapped, so deep RNA sequencing of 100 million reads has 30 million reads for further analysis. The invention uses analytical algorithms that include mapping reads to genomes created for each pathogen based on standard features across large numbers of strains. The computational analysis is enhanced with customized algorithms and improved computing power, shortening the time to primer identification. Workflow is optimized and automated to protect RNA, including PAXgene tubes for phlebotomy.

Deep RNA sequencing identifies pathogen RNA and informs PCR primer. Preliminary data was created using RNA sequencing from COVID-19 patients in the ICU. Data from the deep RNA-sequencing assays indicated limited regions of the viral genome were detected in the bloodstream of COVID-19 patients. This information was used to design primers to validate the sequencing results with a different methodology. cDNA generated from patients' RNA was subjected to quantitative, real-time, reverse transcriptase PCR using two sets of primers for the N gene. One primer pair corresponded to the peak of sequencing reads. Another primer pair was selected at a different site of the gene. See FIG. 1A. Using standard SYBR green methodology, amplicons for the peak N sequence were identified in all tested samples. A template corresponding to the off-peak sequence was detected in only nine of the fifteen patients. When present, the abundance of the off-peak sequence was 4-fold to 16-fold lower than the peak sequence. See FIG. 1A.

This work is important because detecting SARS-CoV-2 in the blood was difficult. Yan, Chang, & Wang (2020).

Deep RNA sequencing can identify RNA from pathogens of interest. Deep RNA sequencing data was taken from two patients with bacteremia due to Escherichia coli infection. The unmapped reads were aligned to the Escherichia coli genome. Each patient had reads that aligned to fourteen genes in TABLE 4. Bacterial ribosomal RNA was identified because the depletion kits are designed for human ribosomal RNA. Although previous work looked at ribosomal RNA for pathogen identification, this method is different because the inventors are looking at RNA and not DNA so the inventors can look for actively expressed genes. Like the probe design for SARS-CoV-2 above, the inventors identify an exact region of the gene of interest covered by the RNA reads identified by the sequencing data. They can target multiple genes with PCR based on those with the most reads in sick patients. The patient with more reads died. The other patient survived. This increase in read counts based on the clinical deterioration could be like a molecular equivalent of time to a positive culture, which is sometimes used clinically. Blackberg et al. (2022).

Patient 2 died of an ESBL Escherichia coli bacteremia. In this patient, genes CTX-M (twelve counts) and blaCTX-M (twelve counts) were identified. These genes result in an ESBL pathogen, confirming the culture diagnosis.

These data show the ability to isolate RNA from the blood, sequence the RNA, and use computational approaches to identify bacterial sequences and create PCR primers to identify infection and resistance.

TABLE 4
Reads of E coli genes per patient with E coli bacteremia

Gene	Patient 1	Patient 2

rrlA 23S	244	12645
rrsB 16S	385	12274
rrlC 23S	232	12205
rrsE 16S	384	12179
rrsA 16S	369	12036
rrsC 16S	446	11447
rrlE 23S	282	11044
rrlB 23S	294	10629
rrlD 23S	248	10462
rrlH 23S	208	10335
rrsG 16S	314	9875
rrsD 16S	308	9717
rrlG 23S	285	9340
rrsH 16S	444	7912
(bla)CTX-M	Negative	Positive

TABLE 5
Test Characteristics of Greatest Importance

Description	Application in this Invention
Rapid: proposed ID test time	A PCR-based test to return a result
of <4 hours for clinical	in less than four hours. This test is
diagnostics that differentially	done on a sample directly from blood
detect and identify species and	by looking at the RNA present from
concurrently or sequentially	the bacteria to detect and identify the
provide phenotypic resistance	species. Because it is looking at RNA,
profile information.	not DNA, a phenotypic resistance
	profile is achieved.
Culture-independent: the	The pathogen identification results
diagnostic should focus on	from information obtained directly
direct detection of the target	from the blood. The PCR targets
pathogens from primary	pathogen RNA, more abundant than
samples	single copies of DNA per organism.
	Culture is not needed.
Technologies capable of	From samples from patients with
detecting drug resistance/	infections due to resistant organisms,
drug-susceptibility, relevant	RNAs corresponding to the resistance
to clinical decision making	gene are measured in the PCR.
	Resistant genes are selected that
	alter antimicrobial therapy for that
	pathogen if they are detected.
Sensitive and specificity	Sensitivity and specificity are studied
should equal or exceed the	and reported according to the Statistical
sensitivity and specificity	Guidance on Reporting Results from
of FDA-cleared tests for	Studies Evaluating Diagnostic Tests -
proposed agents from the	Guidance for Industry and FDA Staff.
same sample type.	Culture-based tests are performed in
	parallel with the molecular tests being
	developed.

In one aspect, unmapped RNA reads from patients with infections that align with pathogens can inform a better diagnostic test. The invention uses deep sequencing (>100 million reads) to identify the most highly expressed RNAs in the blood of patients with bacteremia or pneumonia. Cultures and antibiotic susceptibility testing are performed as the gold standard. RNA sequences from pathogens in transcription analysis are typically discarded because they would not align with the human genome. The inventors identify these unmapped reads in patients' blood and align them to a custom genome derived from the pathogens of interest to identify the causative organism. RNA that aligns with resistance genes is specified. The commonly identified and organism-specific sequences are the template for designing oligonucleotide primers for RT-qPCR tests.

TABLE 6
	Method	Result	Impact
A1a	Deep RNA sequenc-	PCR is validated to	Common bacteria
and	ing identifies RNA	test for bacteremia	causing bacteremia
A1b	to target pathogens	caused by S. aureus,	and pneumonia are
	in infected patients	E. coli,	identified more
	with bacteremia	P. aeruginosa	quickly, allowing
	due to S. aureus,	in samples from	faster administra-
	E. coli,	patients with and	tion of tailored anti-
	P. aeruginosa to	without blood	biotics, improving
	create PCR	infections	survival, and enhanc-
			ing antimicrobial
			stewardship.
A2a	Deep RNA sequenc-	PCR is validated to
and	ing identifies RNA	test for pneumonia
A2b	to target pathogens	caused by S. aureus,
	in infected patients	P. aeruginosa, or
	with pneumonia	H. influenzae in
	due to S. aureus,	samples from
	P. aeruginosa, or	patients with and
	H. influenzae to	without lung
	create PCR	infections
A3a	Deep RNA sequenc-	PCR is validated to	Infections due to
and	ing identifies RNA	test for clinically	resistant organisms
A3b	to target related to	relevant resistance	are identified quickly.
	clinically relevant	genes to S. aureus,	Antibiotics are used
	resistance genes to	E. coli,	immediately, reducing
	create PCR	P. aeruginosa,	the use of broad-
		or H. influenzae	spectrum antibiotics.
		in samples from
		patients with and
		without resistant
		infections

TABLE 7
Microbiological culture positivity rates

		Blood
	Bacteria	Culture	BAL
	Staphylococcus aureus	2.2%	11%
	Escherichia coli	1%	N/A
	Pseudomonas aeruginosa	0.3%	9.5%
	Haemophilus influenzae	N/A	3.7%

Definitions

For convenience, the meaning of some terms and phrases used in the specification, examples, and claims, are listed below. Unless stated otherwise or implicit from context, these terms and phrases shall have the meanings below. These definitions aid in describing particular embodiments but are not intended to limit the claimed invention. All terms should be interpreted in the broadest possible manner consistent with the context when interpreting the disclosure. Unless otherwise defined, all technical and scientific terms have the same meaning as commonly understood by a person having ordinary skill in the art to which this invention belongs. If there is an apparent discrepancy between the usage of a term in the art and its definition provided herein, the definition provided within the specification shall prevail. In general, chemical terminology is found in the International Union of Pure and Applied Chemistry GoldBook. This disclosure is purposefully in commonly understood words, known to a person of skill in the art, but Merriam-Webster's Online Dictionary is used, when appropriate, for terms not specifically demonstrated herein or not known in the art.

Acute respiratory distress syndrome (ARDS) has the biomedical art-recognized meaning. ARDS is a type of respiratory failure characterized by the rapid onset of widespread inflammation in the lungs. Symptoms include shortness of breath, rapid breathing, and bluish skin coloration. Causes may include sepsis, pancreatitis, trauma, pneumonia, and aspiration.

Agent or Therapeutic Agent is a composition of matter that is provided to a subject and suspected to be or involved in a treatment can be a small molecule less than 1000 MW or a large molecule not less than 1000 MW including biologics, oligonucleotides, peptides, oligosaccharides, and larger molecules. Any of the therapeutic agents disclosed herein can be used as or in combination with small molecules and/or large molecules as discussed herein.

Alternative splicing (AS) has the biomedical art-defined meaning. RNA splicing is a molecular function that occurs in all cells directly after RNA transcription but before protein translation, in which introns are removed, and exons are joined. Alternative splicing or alternative RNA splicing, or differential splicing, is a regulated process during gene expression that results in a single gene coding for multiple proteins. Exons of a gene can be included within or excluded from the final, processed messenger RNA (mRNA) produced from that gene. The proteins translated from alternatively spliced mRNAs can contain differences in their amino acid sequence and, often, in their biological functions.

Approximately or About in reference to a value or parameter are generally taken to include numbers that fall within a range of 5%, 10%, 15%, or 20% in either direction (greater than or less than) of the number unless otherwise stated or otherwise evident from the context (except where such number would be less than 0% or exceed 100% of a possible value). A reference to “approximately” or “about” a value or parameter includes (and describes) embodiments that are directed to that value or parameter. For example, description referring to “about X” includes description of “X”.

Comprising means that other elements can be present in addition to the defined elements presented. The use of “comprising” indicates inclusion rather than limitation. The term “including” can be interchanged with “comprising”.

Conjoint Administration and Administered Conjointly refer to any form of administration of two or more different therapeutic compounds such that the second compound is administered while the previously administered therapeutic compound is still effective in the body (e.g., the two compounds are simultaneously effective in the patient, which may include synergistic effects of the two compounds). For example, the different therapeutic compounds can be administered either in the same formulation or in a separate formulation, either concomitantly or sequentially. In certain embodiments, the different therapeutic compounds can be administered at the same time, within one minute, two minutes, four minutes, six minutes, ten minutes, thirty minutes, or an hour or ninety minutes of one another. In some embodiments, the different therapeutic compounds can be administered within one year of one another. Thus, an individual who receives such treatment can benefit from a combined effect of different therapeutic compounds.

Consisting Essentially Of refers to those elements required for a given embodiment. The term permits the presence of additional elements that do not materially affect the basic and functional characteristics of that embodiment of the invention. For example, a pharmaceutical formulation can consist essentially of an active agent and another active ingredient, meaning that a variety of excipients or other additives can be present in the formulation, but no other active pharmaceutical ingredient (API) is present in the formulation, except in formulations wherein an intended synergistic effect is demonstrated by the claims or examples herein (e.g., a formulation consisting essentially of one, two, or three ingredients or pharmaceutically acceptable salts thereof). In another example, a pharmaceutical formulation can consist essentially of an active agent and another ingredient, meaning that the formulation is provided in the form of a nasal spray, an inhaled formulation, an orally administered formulation, or an injection formulation, each of which is tailored for a fast-acting agent, therapeutic agent, or antidote but not tailored for long-term administration, e.g., as a simultaneous treatment. In another example, in the case of a preventative therapy to purposefully prevent a cancer, the opposite, long-term combination therapy, can be referred to with “consisting essentially of”. This example could be applied to, for example, a cancer patient who is best treated by an evolving combination therapy. In another example, the term “consisting essentially of” can be exemplified by plain language provided in the claims.

Consisting Of refers to compositions, methods, and respective components thereof as described herein, which are exclusive of any element not recited in that description of the embodiment.

COVID-19 has the biomedical art-recognized meaning. The SARS-CoV-2 global pandemic has significantly affected global public health. The viral genome is pertinent and relatively small at about 30 kb. Two large overlapping open read frames that encode sixteen nonstructural protein as well as four open reading frames that encode structural proteins. The small size of the genome and few number of gene regions makes for a good size for our analysis. Wu et al., Virology Journal, 20(1), 6 (2023). The outbreak of SARS-CoV-2 that became the COVID-19 global pandemic has had an enormous impact on global health and economics. Cascella et al., Features, Evaluation, and Treatment of Coronavirus (COVID-19). SARS-CoV-2 remains an ongoing threat to human health. EI-Sadr, Vasan, & EI-Mohandes, N. Engl. J. Med., 388(5), 385-387 (2023).

Decrease, Reduced, Reduction, or Inhibit are all used herein to mean a decrease by a statistically significant amount. In some embodiments, Reduce, Reduction, Decrease or Inhibit typically means a decrease by at least 10% as compared to a reference level (e.g., the absence of a given treatment or agent) and can include, for example, a decrease by at least about 10%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 45%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 98%, at least about 99%, or more. Reduction or Inhibition does not encompass a complete inhibition or reduction as compared to a reference level. “Complete inhibition” is a 100% inhibition as compared to a reference level. A decrease can be preferably down to a level accepted as within the range of normal for an individual without a given disorder. In some embodiments, the decrease in the one or more signs or symptoms is evaluated according to the DSM-5. In some embodiments, signs are observed or measured by a health care provider. Symptoms can be reported by the subject. In some embodiments, the decrease of signs or symptoms occurs in less than about 120 minutes, ninety minutes, less than about sixty minutes, less than about thirty minutes, less than about fifteen minutes, less than about ten minutes, or less than about five minutes, or less than about three minutes, or less than about one minute. In some embodiments, the decrease of signs or symptoms occurs in less than one day, less than one week, less than one month, or in less than one year.

Effective Amount, Therapeutically Effective Amount, and Pharmaceutically Effective Amount include an amount sufficient to prevent or ameliorate a manifestation of or a suspected manifestation of a medical condition, such as a cancer or a disease. The manifestation can be a sign or symptom or otherwise. It will be appreciated that there will be many ways known in the art to determine the effective amount for a given application. For example, the pharmacological methods for dosage determination may be used in the therapeutic context. In the context of therapeutic or prophylactic applications, the amount of a composition administered to the subject will depend on the type and severity of the medical condition and on the characteristics of the individual, such as general health, age, sex, body weight and tolerance to drugs. It will depend on the degree, severity, and type of medical condition. The skilled artisan will be able to determine appropriate dosages depending on these and other factors. Examples of other factors can be route of administration and length of administrations. The compositions can be administered in combination with one or more additional therapeutic compounds.

Ensemble Free Energy has the physical art-recognized meaning. Ensemble free energy is estimated based on a partition function algorithm included within the RNAfold program. Lorenz et al., ViennaRNA Package 2.0. Algorithms Mol. Biol., 6, 26 (2011); McCaskill, Biopolymers, 29(6-7), 1105-19 (1990); Zuker & Stiegler, Nucleic Acids Res, 9(1), 133-48 (1981).

Increased, Increase, Enhance, or Activate are all used herein to mean an increase by a statically significant amount In some embodiments, the terms Increased, Increase, Enhance, or Activate can mean an increase of at least 10% as compared to a reference level, for example an increase of at least about 20%, or at least about 30%, or at least about 40%, or at least about 50%, or at least about 60%, or at least about 70%, or at least about 80%, or at least about 90% or up to and including a 100% increase or any increase between 10-100% as compared to a reference level, or at least about a 2-fold, or at least about a 3-fold, or at least about a 4-fold, or at least about a 5-fold or at least about a 10-fold increase, or any increase between 2-fold and 10-fold or greater as compared to a reference level. In the context of a marker or symptom, an “increase” is a statistically significant increase in such level.

Long-Term Administration means that the therapeutic agent or drug is administered for a period of at least twelve weeks. This includes that the therapeutic agent, combination, or drug is administered such that it is effective over, or for, a period of at least twelve weeks and does not necessarily imply that the administration itself takes place for twelve weeks, e.g., if sustained release compositions or long-acting therapeutic agent or drug is used. Thus, the subject is treated for a period of at least twelve weeks. In many cases, long-term administration is for at least 4, 5, 6, 7, 8, 9 months or more, or for at least 1, 2, 3, 5, 7 or 10 years, or more. The administration of the compositions contemplated herein may be carried out in any convenient manner, including by aerosol inhalation, injection, ingestion, transfusion, implantation, application (e.g., topical, otic, or ocular), or transplantation. Administration can be accomplished by an implant. In some embodiments, compositions are administered parenterally. The phrases “parenteral administration” and “administered parenterally” refers to modes of administration other than enteral and topical administration, usually by injection, and includes, without limitation, intravascular, intravenous, intramuscular, intraarterial, intrathecal, intracapsular, intraorbital, intratumoral, intracardiac, intradermal, intraperitoneal, transtracheal, subcutaneous, subcuticular, intraarticular, subcapsular, subarachnoid, intraspinal, and intrasternal injection and infusion. In one embodiment, the compositions contemplated herein are administered to a subject by direct injection into an artery, vein, lymph node, or organ (e.g., heart, muscle, organ).

Mann Whitney U tests have the statistical art-defined meaning. The Mann-Whitney U test (also called the Mann-Whitney-Wilcoxon (MWW), Wilcoxon rank-sum test, or Wilcoxon-Mann-Whitney test) is a nonparametric test of the null hypothesis that it is equally likely that a randomly selected value from one population is less than or greater than a randomly selected value from a second population. This test can investigate whether two independent samples were selected from populations having the same distribution.

Minimum Free Energy has the physical art-recognized meaning. Minimum free energy can be estimated using the minimum free energy algorithm that produces an optimal structure. Zuker & Stiegler, Nucleic Acids Res, 9(1), 133-48 (1981).

Motif Analysis has the biomedical art-recognized meaning. A DNA sequence motif is a subsequence of DNA sequence that is a short similar recurring pattern of nucleotides, and it has many biological functions A DNA motif refers to a short similar repeated pattern of nucleotides that has biological meaning. See Hashim, Mabrouk, & Al-Atabany, Review of different sequence motif finding algorithms. Avicenna J. Med. Biotechnol., 11(2), 130-148 (April-June 2019). The motif analysis tool called XSTREME can be used to input sequences of any length. XSTREME uses two well established motif discovery programs, MEME and STREME, to identify motifs and uses the SEA algorithm for motif enrichment analysis. MEME-ChIP was used to find and analyze motifs and compare with the RNA database. Sequences were entered to the online version 5.5.1 MEME Suite.

mountainClimber is a cumulative-sum-based approach to identifying alternative transcription start (ATS) and alternative polyadenylation (APA) as change points. Unlike many existing methods, mountainClimber runs on a single sample and identifies multiple ATS or APA sites anywhere in the transcript. Cass & Xiao, Cell Systems, 9(4), 23, 393-400.e6 (October 2019).

Next Generation Sequencing (NGS) has the biomedical art-recognized meaning. NGS technology is typically highly scalable, letting the entire genome be sequenced at once. Usually, this is done by fragmenting the genome into small pieces, randomly sampling for a fragment, and sequencing it using various technologies.

Nucleocapsid Gene (N gene) has the biomedical art-recognized meaning of a protein that packages the positive-sense RNA genome of coronaviruses to form ribonucleoprotein structures enclosed within the viral capsid. For example, an N gene can be the gene for the SARS-CoV2-Nucleocapsid (N2) gene. See Wu et al., Virology Journal, 20(1), 6 (2023).

Or means “and/or.” The term “and/or” as used in a phrase such as “A and/or B” herein is intended to include both A and B; A or B; A (alone); and B (alone). Likewise, the term “and/or” as used in a phrase such as “A, B, and/or C” is intended to encompass each of the following embodiments: A, B, and C; A, B, or C; A or C; A or B; B or C; A and C; A and B; B and C; A (alone); B (alone); and C (alone).

Prevents has the biomedical art-recognized meaning. A therapeutic that “prevents” a disorder or condition refers to a compound (or combination) that, in a statistical sample, reduces the occurrence of the disorder or condition in the treated sample relative to an untreated control sample, or delays the onset or reduces the severity of one or more symptoms of the disorder or condition relative to the untreated control sample. For example, a compound or combination that prevents epilepsy may reduce the frequency of seizures and/or reduce the severity of seizures.

Principal Component Analysis (PCA) has the biomedical art-defined meaning. The principal component analysis is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities, each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components.

Prodrug is intended to encompass compounds which, under physiologic conditions, are converted into the therapeutically active agents of the present invention (e.g., any compound selected from this disclosure). A common method for making a prodrug is to include one or more selected moieties which are hydrolyzed under physiologic conditions to reveal the desired molecule. In other embodiments, the prodrug is converted by an enzymatic activity of the host animal. For example, esters or carbonates (e.g., esters or carbonates of alcohols or carboxylic acids) are preferred prodrugs of the present invention. In certain embodiments, some or all of the compounds selected from this disclosure in a formulation can be replaced with the corresponding suitable prodrug, e.g., wherein a hydroxyl in the parent compound is presented as an ester or a carbonate or carboxylic acid present in the parent compound is presented as an ester. In some embodiments, a “prodrug” is made by using an absorbing particle that subsequently releases an active form after administration.

Protecting Group refers to a group of atoms that, when attached to a reactive functional group in a molecule, mask, reduce or prevent the reactivity of the functional group. A protecting group may be selectively removed as desired during the course of a synthesis. Examples of protecting groups can be found in Greene's Protective Groups in Organic Chemistry, Wuts, editor (John Wiley & Sons, 2014), and in Harrison et al., Compendium of Synthetic Organic Methods, Vols. 1-8. Examples of representative nitrogen protecting groups include, but are not limited to, formyl, acetyl, trifluoroacetyl, benzyl, benzyloxycarbonyl (CBZ), tert-butoxycarbonyl (Boe), trimethylsilyl (TMS), 2-trimethylsilyl-ethanesulfonyl (TES), trityl and substituted trityl groups, allyloxycarbonyl, 9-fluorenylmethyloxycarbonyl (FMOC), nitro-veratryloxycarbonyl (NVOC) and the like. Examples of representative hydroxyl-protecting groups include, but are not limited to, those where the hydroxyl group is either acylated (esterified) or alkylated such as benzyl and trityl ethers, as well as alkyl ethers, tetrahydropyranyl ethers, trialkylsilyl ethers (e.g., TMS or TIPS groups), glycol ethers, such as ethylene glycol and propylene glycol derivatives and allyl ethers.

Read has the biomedical art-defined meaning of reading sequencing results to determine nucleotide base structure.

Read Origin Protocol (ROP) has the computer-art meaning of a computational protocol to discover the source of all reads, including those originating from repeat sequences, recombinant B and T cell receptors, and microbial communities. The Read Origin Protocol was developed to determine what the unmapped reads represented. Mangul al., Genome Biology 19, 36 (2018). The recent development of the Read Origin Protocol (ROP) has shown unmapped reads align to bacterial, viral, fungal, and B/T rearrangement genomes.

RNA Sequencing (RNA-Seq) has the biomedical art-recognized meaning of a sequencing technique that uses next-generation sequencing (NGS) to reveal the presence and quantity of RNA in a biological sample, representing an aggregated snapshot of the cells' dynamic pool of RNAs, also known as transcriptome. In RNA-Seq, reads may align primarily over certain areas and it is common that there are read sequences that are repeatedly identical. Deschamps-Francoeur, Simoneau, & Scott, Handling multi-mapped reads in RNA-seq., Comput. Struct. Biotechnol. J., 18, 1569-1576 (2020). Several factors determine the extent of repeated sequences, especially with the many processing steps in the RNA-Seq procedure. Fu et al., BMC Genomics, 19(1), 531 (2018).

RNAfold is a computer program from the ViennaRNA Package that is used to predict the minimum free energy of the secondary structure of RNA-Seq read sequences. RNAfold uses a loop-based energy model and dynamic program algorithm to estimate the MFE based on an RNA sequence. Lorenz et al., ViennaRNA Package 2.0. Algorithms Mol. Biol., 6, 26 (2011).

Sepsis has the medical art-defined meaning of a life-threatening condition that arises when the body's response to infection injures its tissues and organs. Bone et al., Chest, 101, 1644-1655 (1992); Singer et al., JAMA, 315, 801-810 (February 2016). Sepsis is defined by altered physiology leading to organ and immune system dysfunction due to an infection Singer et al., The Third International Consensus definitions for sepsis and septic shock (Sepsis-3). JAMA, 315(8), 801-10 (February 2016).

STAR Aligner is the Spliced Transcripts Alignment to a Reference (STAR), a fast RNA-seq read mapper with support for splice-junction and fusion read detection. Using a Suffix Array index, STAR aligns reads by finding the Maximal Mappable Prefix (MMP) hits between reads (or read pairs) and the genome. Different parts of a read can be mapped to different genomic positions, corresponding to splicing or RNA-fusions. The genome index includes known splice junctions from annotated gene models, allowing for sensitive detection of spliced reads. STAR performs local alignment, automatically soft clipping ends of reads with high mismatches. Dobin et al., Bioinformatics, 29(1), 15-21 (January 2013).

Statistically Significant or Significantly refers to statistical significance and generally means a two-standard deviation (2SD) or greater difference.

Subject has the biomedical art-related meaning. A subject may or may not be aware of suffering from a cancer or a disease condition. A health care provider may suspect a disease or cancer or may have confirmed cancer or disease. A subject can be one who was previously diagnosed with or identified as suffering from or having a condition in need of treatment (e.g., a cancer or related disorder) or one or more complications related to such a condition, and optionally, but need not have already undergone treatment for a condition or the one or more complications related to the condition. Alternatively, a subject can be one who has not been previously diagnosed as having a condition in need of treatment or one or more complications related to such a condition. For example, a subject can be one who exhibits one or more risk factors for a condition, or one or more complications related to a condition or a subject who does not exhibit risk factors. A “subject in need” of treatment for a particular condition can be a subject having that condition, diagnosed as having that condition, suspected as having, or at risk of developing that condition. In another example, the subject was brought into a treatment situation entirely without the subject's knowledge and/or intent. A subject can need treatment but not be responsive to a treatment, and as described herein the present methods and formulations may save the subject's life. Subject refers to a mammal, including but not limited to a dog, cat, horse, cow, pig, sheep, goat, rodent, or primate. Subjects can be house pets (e.g., dogs, cats), agricultural stock animals (e.g., cows, horses, pigs, chickens, etc.), laboratory animals (e.g., mice, rats, rabbits, etc.), but are not so limited. Subjects particularly include human subjects in urgent treatment as described herein. The human subject may be a pediatric, adult, or a geriatric subject. The human subject may be of any sex.

Treat, Treatment, Treating, or Amelioration when used in reference to a disease, disorder, or medical condition, refer to therapeutic treatments for a condition, wherein the object is to reverse, alleviate, ameliorate, inhibit, slow down or stop the progression or severity of a symptom or condition. The term “treating” includes reducing or alleviating at least one adverse effect or symptom of a condition. Treatment is generally “effective” if one or more symptoms or clinical markers are reduced. Alternatively, treatment is “effective” if the progression of a condition is reduced or halted. That is, “treatment” includes not just the improvement of symptoms or markers, but also a cessation or at least slowing of progress or worsening of symptoms that would be expected in the absence of treatment. Beneficial or desired clinical results include, but are not limited to, alleviation of one or more symptoms, signs, diminishment of extent of the deficit, stabilized (i.e., not worsening) state of a symptom or condition, delay or slowing of onset of symptoms or indications, and an increased lifespan as compared to that expected in the absence of treatment. Treating includes prophylactic and/or therapeutic treatments. The terms “prophylactic” or “therapeutic” treatment is biomedical art-recognized and includes administration to the host of one or more of the subject compositions. If administered before clinical manifestation of the unwanted condition, e.g., disease or other unwanted state of the host animal, then the treatment is prophylactic, i.e., protects the host against developing the unwanted condition. If it is administered after manifestation of the unwanted condition, the treatment is therapeutic, i.e., intended to diminish, ameliorate, or stabilize the existing unwanted condition or side effects thereof.

Treatment For Sepsis has the medical art-recognized meaning. Sepsis is treatable, and timely implementation of targeted interventions improves outcomes. The Mayo Clinic informs the public that several medications are used in treating sepsis and septic shock. They include antibiotics. Broad-spectrum antibiotics, which are effective against various bacteria, are usually used first. After learning the results of blood tests, a doctor may switch to a different antibiotic that is targeted to fight the specific bacteria causing the infection. Other medications include low doses of corticosteroids, insulin to help maintain stable blood sugar levels, drugs that modify the immune system responses, and painkillers or sedatives.

Whippet (OMICS_29617) is a program that enables the detection and measurement of alternative RNA splicing events of any complexity with computational requirements compatible with a laptop computer. Whippet applies the idea of lightweight algorithms to event-level splicing measurement by RNAseq. The software can help with the analysis of simple to complex alternative splicing events that function in normal and disease physiology. Alternative splicing events with high entropy are identified using Whippet. Sterne-Weiler et al., Molecular Cell, 72, 187-200.e186 (2018). Whippet can generate an entropy value for each gene's identified alternative splicing and transcription event. These entropy values are created with no groups used in the gene expression analysis. To visualize this data, a principal component analysis (PCA) can be conducted to reduce the dataset's dimensionality and obtain an unsupervised overview of trends in entropy values among the samples. Raw entropy values from all samples can be concatenated into one matrix, and missing values were replaced with column means. Mortality can be overlaid onto the PCA plot to assess the ability of these raw entropy values to predict this outcome in this sample set. This analysis was done in R (version 3.6.3).

In this specification and the appended claims, the singular forms “a,” “an” and “the” include plural referents unless the content clearly dictates otherwise. For example, reference to “a cell” includes a combination of two or more cells, and the like.

The specification does not concern a process for cloning humans, methods for modifying the germ line genetic identity of humans, uses of human embryos for industrial or commercial purposes, or procedures for modifying the genetic identity of animals likely to cause them suffering with no substantial medical benefit to man or animal and animals resulting from such processes.

Guidance Regarding Materials and Methods

A person of ordinary skill in the biomedical art can use these materials and methods as guidance to predictable results when making and using the invention:

Human subjects. The inventors have timely access to the samples in sufficient quantities. The inventors are enrolling patients in the Intensive Care Units with sepsis and sending their blood for deep RNA sequencing. After Institutional Review Board approval, patients are recruited for this research program from the emergency department and hospital patients when blood cultures are ordered. Through alerts from the electronic health record (EPIC), research assistants are notified of when blood cultures are ordered. Patients have consented before the collection of the blood culture. Samples are drawn in collaboration with the phlebotomy service and the bedside nurse. Blood is collected in two PAXgene tubes, 5 mL of blood, and stored in an −80° C. freezer until RNA is isolated for sequencing. The last six months of data in the hospital were reviewed. Many samples were available. Over the six-month time course, 2,453 patients had blood cultures drawn in the emergency department, and 602 patients had blood cultures drawn in the Intensive Care Units. Blood is collected from patients who undergo bronchial alveolar lavage (BAL) in the Intensive Care Unit to diagnose pneumonia. Samples are collected before the bronchial alveolar lavage and stored as described. Over the six-month time course, forty-six patients had bronchial alveolar lavage samples obtained in the emergency department and fifty-one patients had bronchial alveolar lavage samples obtained in the Intensive Care Units.

In EXAMPLE 7, research protocols were approved by the Lifespan Institutional Review Board in accord with the Declaration of Helsinki. Participants or their legally authorized representatives provided written informed consent before enrollment.

Biological variables. Both sexes are recruited. Variables such as age (patients are included across the lifespan, weight, and medical co-morbidities are collected and compared across groups. If these variables, or sex, are significantly different (t-test or rank sum), the analysis will adjust these factors via regression.

Variables such as age (patients are included across the lifespan, weight, and medical co-morbidities are collected and compared across groups. If these variables, or sex, are significantly different (t test or rank sum), these factors are adjusted for in the analysis via regression.

Blood sample collection. Blood samples are collected on Day 0 of Intensive Care Unit admission. Clinical data including COVID specific therapies was collected prospectively from the electronic medical record and participants were followed until hospital discharge or death. Ordinal scale can be collected as described by Beigel et al., New England Journal of Medicine (2020); along with sepsis and associated sequential organ failure assessment (SOFA) score, and the diagnosis of ARDS. See Singer et al., The Third International Consensus definitions for sepsis and septic shock (Sepsis-3). JAMA, 315: 801-810 (2016); Ferguson et al., The Berlin definition of ARDS. Intensive Care Medicine, 38, 1573-1582 (2012).

RNA extraction and sequencing. Whole blood can be collected in PAXgene tubes (QIAGEN, Germantown, MD, USA) and sent to Genewiz (South Plainfield, NJ, USA) for RNA extraction, ribosomal RNA depletion and sequencing. Sequencing can be done on Illumina HiSeq machines to provide 150 base pair, paired end reads. Libraries were prepared to have three samples per lane. Each lane provided 350 million reads ensuring each sample had >100 million reads.

RNA isolation and sequencing. Blood from patients is collected using the PAXgene tubes (PreAnalytiX, Switzerland). All samples require at least 1400 nanograms of RNA for deep sequencing. With the PAXgene system, one routinely obtains >3000 nanograms. After RNA samples are processed, they are sent out for RNA sequencing. Due to the high concentration of globin and ribosomal RNA in blood samples, these samples are then further processed at the sequencing company to reduce globin RNA and human ribosomal RNA. This optimizes the yield of clinically relevant reads. Each sample are sent out for deep RNA sequencing with a goal of 100 million reads per sample.

RNA sequencing is done on non-CLIA machines because this data is not used in clinical practice. The vendor has Clinical Laboratory Improvement Amendment (CLIA) certified machines to allow for ease of translation in future studies. Not all the blood samples collected are sent for deep RNA sequencing. One of the two PAXgene tubes are kept for the PCR tests.

Sample size calculation. Patients with bacteremia are compared to patients without bacteremia to identify targets for the creation of the PCR. Based on the positive culture rate (TABLE 6), the inventors would collect 2200 blood cultures to obtain fifty that are positive for Staphylococcus aureus. These rates are for all samples. The inventors are targeting the collection for the Emergency Department and the Intensive Care Units, so the positive rate is higher. 3500 samples are obtained to get a representation of each type of organism. The institution averages 3000 blood cultures in the Emergency Department and the Intensive Care Units every six months. This testing results in at least sixty patients with Staphylococcus aureus, thirty with Escherichia coli, and ten with Pseudomonas æruginosa. All samples with a corresponding positive blood cultures for these three pathogens are sent for deep RNA sequencing. The inventors send samples for RNA sequencing with a corresponding positive blood culture, including those judged to be contaminants, for about 135, with an additional 115 of samples from patients with negative blood cultures. This process would result in 350 samples sent for RNA sequencing for EXAMPLE 1.

The second PAXgene tube drawn on these patients is used to verify the PCR tests. Patients with pneumonia are compared to patients without pneumonia to identify targets for the creation of the PCR. Based on the positive culture rate (see TABLE 6), the inventors would ideally collect all patients with a bronchial alveolar lavage from the Emergency Department or Intensive Care Unit. Over six months, this process would include about 100 patients and would result in eleven with Staphylococcus aureus, ten with Pseudomonas æruginosa, and four with Hæmophilus influenzae. The inventors collect eighteen months of samples to obtain about 300 blood tubes to sequence for the pneumonia section of the invention. Because two pathogens are being studied, these patients have complementary bronchial alveolar lavage and blood cultures sent simultaneously. Resistance genes are identified using the same samples collected.

Assessment of clinical information. RNA sequencing data are interpreted with clinical data collected from the electronic medical record including endpoints such as mortality, Intensive Care Unit length of stay, hospital length of stay, SOFA score (Shankar-Hari et al. (2016)), ventilator days, renal failure, ARDS (Ferguson et al. (2012)). Culture data are based on the test results in the microbiology lab and are the gold standard. Clinical response to antibiotics is tracked to see if the treatment based on microbiology data is correct. Changes in treatment are assessed to ensure culture data is used in treatment and antimicrobial stewardship practices are being followed.

Polymerase chain reaction (PCR) design. Optimized PCR parameters ensure accuracy and reproducibility in qPCR reactions. See Bustin & Huggett (2017) and Bustin, Mueller, & Nolan (2020)). The preliminary data show bacterial reads are measurable from patient with bacteremia and pneumonia and that the reads can be aligned to the organism's genome. RNA sequencing data accumulated from patients with bacteremia or pneumonia due to the specified pathogens are used to identify sequences of interest. These sequences are compared to a pan genome of the same organism to confirm the target is generalizable to the pathogen. Wang et al. (2022). The inventors use Beacon Designer (Premier Biosoft) to design several primer/probe combinations for the sequences, the specificity of which are confirmed by BLAST searches. Primers with low specificity, dimer formation, or that create amplicons with complex secondary structures are excluded. Bustin & Huggett (2017). Primer-BLAST (NCBI) are used as an independent, complementary design strategy; primers identified by both approaches are prioritized. PCR reactions are optimized in the laboratory for temperature and primer concentration for the master mix. The goal is to create a standard set of testing conditions.

Testing the PCR. The PCR tests are validated in two ways. First, cDNA libraries used for RNA sequencing are tested. Next, RNA from the blood of patients, both with and without the infection, are used as templates for cDNA synthesis and then PCR. PCRs are applied to the samples from RNA sequencing and an independent cohort of patients to validate the assays. Several primer combinations are evaluated for each target sequence. Bustin & Huggett (2017). SYBR green methodology are used to prioritize different primer combinations. Hydrolysis (Taqman) probes for qPCR, which were already designed with the primers, is then synthesized for the prioritized primer combinations.

Rigor and reproducibility. The preliminary data show isolated RNA from patients and high quality RNA sequencing results. The inventors also focus on isolation methods that are standard and can easily be applied followed so the results can be translated to clinical practice. To enhance robustness during development, it is standard practice for each step of the PCRs (setup, cycling, analysis) to be performed in separate rooms, reducing reactions being contaminated with amplicons from past runs.

Computing resources. Computational biology work is performed on servers on premise. These servers are secured because they contain clinical data. All HIPAA standards are applied. The server operates on 6× VxRail E560F nodes (PowerEdge R640 1 U rack mount servers) and has dual Intel Xeon Platinum 8260 (24c) 2.4 Ghz with 1,152 GB RAM, 2×1.6 TB SAS SSD cache, 8×7.68 TB SAS SSD capacity, 4×10 Gb data ports, and 1×1 Gb iDRAC management port. This server includes vSphere Enterprise Plus with 3 Years 24×7 Mission Critical Support per node configured to provide the computational infrastructure. The server consists of 288c (691.2 GHz) CPU, and 6.75 TB RAM. Storage estimates reflect 368.64 TB RAW/222 TB usable memory on a RAID6 configuration with 20% vSAN overhead. This server manages all large data sets from RNA sequencing. Due to the depth of sequencing for RNA splicing analysis (100 million reads vs. 40 million), more data is generated from both sequencing and analysis. In a preliminary project, the inventors generated one terabyte of sequencing data and another terabyte from the alignment to the genome. Because RNA sequencing data is always identifiable, the data from humans are treated as though it is protected health information (PHI), even though none of the typical identifiers (such as name, date of birth, etc.) are associated with the data.

The following pipeline encompasses the typical analysis: differential expression, RNA analysis is done with Whippet (Sterne-Weiler et al. (2018)). The unmapped reads are then analyzed for microbial RNA. The inventors curate a reference genome of all identified species of Staphylococcus aureus, Escherichia coli, Pseudomonas æruginosa, and Hæmophilus influenzae. This are done using genomes described in TABLE 4 with the addition of plasmids. Bacterial rearrangements are common across strains. This tool adjusts for rearrangement with a consensus genome to align the un-mapped reads to them. Noureen, Tada, Kawashima, & Arita (2019). This tool allows for visualization and construction of a consensus genome. The conserved and strain specific sequences are kept. Tada, Tanizawa, & Arita (2017). Targets are preferentially chosen from conserved regions. Strain specific targets are used if clinically relevant. Specific resistance genes are be searched for in the unmapped reads using the STAR aligner.

Cloud based computing. Due to the depth of sequencing RNA splicing analysis (100 million reads vs. forty million), more data is generated from both sequencing and analysis (a small study generated one terabyte of sequencing data and another terabyte from the alignment to the genome). With such a large amount of data predicted, the ability to expand and contract the storage space and computing power in the cloud is the ideal choice. This server stores and analyzes data from both mouse and human samples. Because RNA sequencing data is always identifiable, the data from humans are treated as though it is protected health information (PHI), even though none of the typical identifiers, such as name, date of birth, etc., are associated with the data. The cloud server is only accessible through a hospital virtual desktop and data are saved only to the Azure server or a hospital computer. Data are encrypted while stored, and when in transit to or from the hospital. Any link to typical identifiers is kept separate from the sequencing data. The cloud-based server allows for large data analysis with computing and storage needs changing on a per-use basis. The Azure server is Linux based and uses programming in R and Python. The following pipeline encompasses the typical analysis: differential expression, RNA analysis is done with Whippet. This includes an entropy measure, and genes of interest undergo gene ontology term analysis. Genes with alternative transcription start and end sites identified through Whippet are correlated with findings from the mountainClimber analysis.

Computational analysis and statistics. RNA sequencing data was first checked for quality using FASTQC. RNA sequencing data collected from the GTEx consortium and analyzed with the Whippet software for differential gene processing. Alternative transcription events are those events identified by Whippet as ‘tandem transcription start site,’ ‘tandem alternative polyadenylation site,’ ‘alternative first exon,’ and ‘alternative last exon.’ Alternative RNA splicing events are those events labeled ‘core exon,’ ‘alternative acceptor splice site,’ ‘alternative donor splice site,’ and ‘retained intron.’ Alternative mRNA processing events were determined by a log 2 fold change of greater than 1.5+/−0.2. Statistical significance was calculated by the chi-square p-value of a contingency table based on 1000 simulations of the probability of each result.

Computational biology and statistical analysis. All computational analysis can be done blinded to the clinical data. The data can be assessed for quality control using FastQC. See Andrews, FastQC: A quality control tool for high throughput sequence data. (2014). RNA sequencing data can be aligned to the human genome using the STAR aligner. Dobin et al., Bioinformatics (Oxford, England), 29, 15-21 (2013). Reads that aligned to the human genome can be separated and called ‘mapped’ reads. Reads that do not align to the human genome, which are typically discarded during standard RNA sequencing analysis, were identified as ‘unmapped’ reads. The unmapped reads then align to the relevant comparator and counted per sample using Magic-BLAST. See Boratyn et al., BMC Bioinformatics, 20, 405 (2019). The unmapped reads were further analyzed with Kraken2. See Wood, Lu, & Langmead, Genome Biology, 20, 257 (2019). The analysis used the PlusPFP index to identify other bacterial, fungal, archaeal, and viral pathogens. See Kraken2/Bracken Refseq indexes maintained by BenLangmead, which uses Kutay B. Sezginel's modified version of the minimal GitHub pages theme.

Reads that align to the human genome, the mapped reads, can undergo analysis for gene expression, alternative RNA splicing, and alternative transcription start/end by Whippet. See Sterne-Weiler et al., Molecular Cell, 72, 187-200.e186 (2018). When comparisons are made between groups (died vs. survived) differential gene expression can be set with thresholds of both p<0.05 and +/−1.5 log 2 fold change. Alternative splicing was defined as core exon, alternative acceptor splice site, alternative donor splice site, retained intron, alternative first exon and alternative last exon. Alternative transcription start/end events can be defined as tandem transcription start site and tandem alternative polyadenylation site. Alternative RNA splicing and alternative transcription start/end events can be compared between groups. See Sterne-Weiler et al., Molecular Cell, 72, 187-200.e186 (2018). Significance was set at great than 2 log 2 fold change as described by Fredericks et al., Intensive Care Medicine (2020). Genes identified from the analysis of mapped reads can be evaluated by GO enrichment analysis (PANTHER Overrepresentation released 2020-07-28). See Mi et al., Nature Protocols, 8, 1551-1566 (2013).

Kraken2. These tools are compatible with both Kraken1 and Kraken2. Both tools help users in analyzing and visualizing Kraken results. Bracken lets users estimate relative abundances within a specific sample from Kraken2 classification results. Bracken uses a Bayesian model to estimate abundance at any standard taxonomy level, including species/genus-level abundance. Pavian was developed as a comprehensive visualization program that can compare Kraken2 classifications across multiple samples. KrakenTools is a suite of scripts to help analyze Kraken results. For more information, a person having ordinary skill in the biomedical art can refer to Wood, Lu, & Langmead, Improved metagenomic analysis with Kraken2, Genome Biology (Nov. 28, 2019).

In EXAMPLE 5, the inventors present an analysis of the stability of targets to diagnose COVID-19. RNAfold from the ViennaRNA Package was used to predict the minimum free energy of the secondary structure of RNA-Seq read sequences. RNAfold was used to calculate the minimum free energy value of the structures and ensemble free energy values to compare stability between different read sequences. Energy parameters for calculations were set at 37° C. Different statistical tools were used to assess the stability or instability of a secondary structure in addition to RNAfold.

Sequences that were outliers in length were disregarded for analysis. Only sequences that were less than 175 nucleotides long were analyzed. Energy parameters for calculations were set at 37° C. Statistical analysis was computed using R Studio. A Welch ANOVA was first performed to compare the minimum free energy (MFE) values and ensemble free energy (EFE) values for reads that were located within known gene regions.

Allocation to a certain gene region was determined by which gene region the middle of the read sequence was located. A Games-Howell post hoc test was performed for pairwise comparison of free energy values between genes. A similar statistical analysis was conducted on the nucleocapsid (N) gene.

A Welch T-test was performed to compare the minimum free energy and ensemble free energy of the sequences with and without the motif. Sequences inputted into MEME-ChIP are ideally 500 letters in length while the longest read sequences used in our analysis was 151 nucleotides long. MEME-ChIP was used to find only the first ten motifs. Analysis of the effect of a destabilizing motif on quantity of duplicated reads was conducted using a negative binomial regression.

A Welch ANOVA was first performed to compare the minimum free energy values and the ensemble free values for reads at the beginning, middle, or end of the N gene. The N gene was divided into three equally long regions, and allocation to the region of the N gene was determined by the middle of the read sequence. A Games-Howell post hoc test was performed for pairwise comparison of free energy values between regions of the N gene. Because the early gene region for the N gene had zero variance, a Welch t-test was conducted to compare individual within gene regions. A Chi-Squared goodness of fit test was performed to assess the distribution of reads among the genes. This established if certain genes had more or fewer reads relative to other genes.

Gene ontology (GO) was assessed using The Gene Ontology Resource Knowledgebase. Ashburner et al., Nature Genetics, 25, 25-29 (2000); The Gene Ontology Resource. Nucleic Acids Research, 47, D330-d338 (2019). Genes from the analyses were entered, and outputs were displayed. Outputs from gene ontology do not correlate with actual increase or decrease in a gene's expression but are related to expected based upon the set of genes entered.

Pipeline. The following pipeline encompasses the typical analysis: differential expression, RNA analysis is done with Whippet (Sterne-Weiler et al. (2018)). The unmapped reads are analyzed for microbial RNA. We curate a reference genome of all identified species of Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, and Hæmophilus influenzae. Bacterial rearrangements are common across strains. This tool adjusts for rearrangement because we make a consensus genome to align the unmapped reads to them. Noureen, Tada, Kawashima, & Arita (2019). This tool allows for the visualization and construction of a consensus genome. The conserved and strain-specific sequences are kept. Tada, Tanizawa, & Arita (2017). Targets are preferentially chosen from conserved regions. Strain-specific targets are used if clinically relevant. Specific resistance genes are searched for in the unmapped reads using the STAR aligner.

The following EXAMPLES are provided to illustrate the invention and should not be considered to limit its scope in any way.

Example 1

Design a Direct from Blood, without Culture, Reverse Transcriptase Polymerase Chain Reaction (RT-qPCR) Test for Bacteria Causing Bacteremia, Specifically Staphylococcus aureus, Escherichia Coli, Pseudomonas æRuginosa, Based on the RNA Identified in Patients with Bacteremia Caused by these Organisms (A1a).

Rationale. Blood cultures are the current gold standard for pathogen diagnostics but take days. Blood cultures have a known contaminant rate, which can adversely affect treatment and disease progression, as shown in the COVID pandemic. Yu et al. (2020).

RNA sequencing is an emerging technology that can enhance the diagnostic capabilities. Unmapped reads, i.e., reads that do not align to the human genome, are typically discarded in RNA sequencing data from humans. When the depth of the RNA sequencing is enough, these unmapped reads can provide useful clinical information. The unmapped reads found in the blood of patients with bacteremia are used to inform the development of a diagnostic PCR.

The gene expression of the bacteria discriminates between infection and simply colonization. D'Mello et al., Proceedings of the National Academy of Sciences (Dec. 29, 2020). Targeting RNA is more specific than DNA by eliminating the signals of free DNA from dead bacteria or pathogen DNA released from immune cells combating the infection. Opota, Jaton, & Greub (2015).

TABLE 8
Creation of bacterial genomes

Bacteria	Genomes	Plasmids

Staphylococcus aureus	https://www.ncbi.nlm.nih.gov/genome/154	958
Escherichia coli	https://www.ncbi.nlm.nih.gov/genome/167	5895
Pseudomonas aeruginosa	https://www.ncbi.nlm.nih.gov/genome/187	155
Haemophilus influenzae	https://www.ncbi.nlm.nih.gov/genome/165	1

Assay 1. Assess the RNA sequencing data from patients with blood infections due to Staphylococcus aureus, Escherichia coli, and Pseudomonas æruginosa. Unmapped reads or reads that do not align to the organism of interest, are typically discarded. These reads are used to identify bacterial RNA in the blood. This was initially done using Kraken2. Wood, Lu, & Langmead (2019). For more granularity, the inventors assembled custom genomes to which the unmapped reads are aligned using STAR RNA-sequencing aligner. Dobin et al., Bioinformatics, 29(1), 15-21 (January 2013). These genomes are based on the common genome in TABLE 4 but also include sequences from other chromosomes and the plasmids attributed to those bacteria, creating a pan-genome. Eizenga et al. (2020). Samples from patients with Staphylococcus aureus are used to identify the significant reads that align to this bacterium and repeated for other pathogens of interest. This gives a total read count for each bacterium and the parts of the genome with the most abundant reads. From these abundant reads, PCR primers test for the pathogens based on large reads of common areas across many patients.

Assay 2. Create RT-qPCR primers to identify Staphylococcus aureus, Escherichia coli, and Pseudomonas æruginosa causing bacteremia. Using the targets of interest from the deep RNA sequencing data, PCR primers cover these parts of the bacterial genome identified. Multiple primers for multiple targets can identify one pathogen. This are done through multiplexing with the NeuMoDx instrument. The target of these primers is the RNA in the blood. A reverse transcriptase reaction is used to create the cDNA for the PCR.

Expected results. The preliminary data show that patients with bacteremia have bacterial RNA in their blood that correlates with the causative organism (TABLE 4) There should be a set of highly expressed genes from each of the bacteria during infection that can be the basis for identification. The inventors expect genes like the coagulase gene to be detected in patients with Staphylococcus aureus bacteremia. Cheng et al., PLoS Pathogens, 6(8), e1001036 (2010). The inventors prioritize PCR targets unique to the bacteria being tested and distinct from other bacteria. More findings include observations that gene expression of the bacteria can determine colonization versus infection based on expression pattern and abundance. D'Mello et al., Proceedings of the National Academy of Sciences (Dec. 29, 2020). The number of reads, i.e., transcript abundance, may correlate with patient condition or patient outcomes. Abundant clinical data is associated with patients from with the samples are derived. Read frequency or abundance on RT-qPCR are evaluated for these correlations.

Potential alternatives. The bacteria identified in the sequencing may not be correlated to microbiology culture. This could be due to blood cultures being negative in 50% of blood stream infections, due to low numbers of bacteria in the blood or the impact of antibiotics before the sample is obtained. Opota, Jaton, & Greub (2015). Blood culture could identify the wrong pathogen while another pathogen could cause infection, i.e., a contaminant. the approach includes aligning unmapped reads using Kraken2 to identify background levels of sequences from unrelated bacteria that could be commensals or contaminants. A single gene may not uniquely identify an organism, reducing specificity of the test. In that situation, the inventors test gene combinations as described above. Alternatively, unique alleles/SNPs are used to define a specific pathogen. Established techniques are used to measure SNPs in the RT-qPCR format.

Example 2

Validate these RT-PCR Tests in Samples from Patients with and without Bacteremia (A1b).

Rationale. RT-qPCR allow for the identification of pathogens, directly from blood, without culture, in less than four hours. RT-qPCR allow for faster, pathogen-directed antibiotic selection. Blood culture collection is recommended before antibiotic administration to enhance the diagnostic sensitivity of the blood culture. See Evans et al., Critical Care Medicine, 49(11), e1063-e1143 (Nov. 1, 2021). With this diagnostic test proposed here, antibiotics are not expected to influence the RNA present at the blood draw.

Assay 1. Test PCR primers on samples used for RNA sequencing. The cDNA libraries created for RNA sequencing are accessed as the initial test of the PCR primers. Because the RNA sequencing data determined these RNA segments were present, this are the first step in assessment of the utility of these PCR tests for the bacteria. The cDNA from all samples with positive cultures for each of the bacteria are used for this assay. Each cDNA sample are tested using the PCR primers for all pathogens. As a negative control for specificity, the inventors collect cDNA from the blood of patients and normal controls that have no infections.

Assay 2. Validate PCR primers on samples that mimic collection for clinical use. To obtain sensitivity and specificity in line with FDA requirements, samples from patients with and without confirmed blood infections due to the pathogens of interest are identified from banked clinical specimens, including PAXgene tubes. The PAXgene tubes are being collected at the time of blood culture collection and stored. In the experience, PAXgene tubes stabilize high quality RNA. To enhance robustness of the testing, these tubes are blinded to the team performing the PCR assays. The RNA is extracted and globin and rRNA are reduced using commercially available kits. cDNA libraries are made with reverse transcriptase and then PCR are done with the primers. PAXgene tubes are used but the RT-qPCR are done immediately to ensure the result returns in less than four hours.

Expected results. A panel of PCR primers are developed and optimized on a machine that can be easily translated to a clinical microbiology laboratory. The tests identify Staphylococcus aureus, Escherichia coli, and Pseudomonas æruginosa directly from the blood through extraction of RNA, reduction of globin and rRNA, and creation of cDNA for the PCR. These tests have sensitivity and specificity in line with requirements of the FDA. These direct from blood PCR are initially done for Staphylococcus aureus, Escherichia coli, and Pseudomonas æruginosa. Through collecting samples from patients in PAXgene tubes with other infections, the PCR panel can be expanded as new targets from more pathogens are identified. PCR is rapid and could monitor treatment impact, a practice not currently done as culture takes days to return. If successful treatment is detected, antibiotic course could be shortened and enhance antimicrobial stewardship.

Potential alternatives. Blood interferes with PCR when identifying DNA, but not RNA. Sidstedt et al. (2018). Deep RNA sequencing may find a gene that identifies infection, but PCR conditions cannot be optimized to replicate the finding. This problem can be solved when RNA sequencing costs and time are reduced. RNA sequencing should take less than four hours at a depth of 100 million reads or more.

Example 3

Design a direct from blood, without culture, reverse transcriptase polymerase chain reaction (RT-PCR) test for bacteria causing pneumonia, specifically Staphylococcus aureus, Pseudomonas æruginosa, and Hæmophilus influenzae, based on the RNA identified in patients with pneumonia caused by these organisms (A2a).

Rationale. The diagnosis of hospital-acquired pneumonia is complex. Modi & Kovacs (2020). Bronchial alveolar lavage (BAL) is the gold standard, like blood culture for bacteremia. Because bronchial alveolar lavage requires an invasive intervention (bronchoscopy) that can worsen the clinical picture, screening tools are used to decide when to perform them, hence the yield is higher than blood culture. A direct from blood test that provides the same diagnosis obviates the need for the invasive bronchoscopy intervention. The assays described below parallel those in EXAMPLE 1 with an independent cohort of patients diagnosed with pneumonia and who undergo BAL.

Assay 1. Assess the RNA sequencing data from patients with pneumonia due to Staphylococcus aureus, Pseudomonas æruginosa, and Hæmophilus influenzae. As described above for the blood infections, unmapped reads are aligned to genomes of interest (TABLE 4) to identify genes with increased expression in patients with infection diagnosed by BAL. The blood is collected in PAXgene tubes at the time of BAL. Staphylococcus aureus and Pseudomonas æruginosa genes that are identified are compared to the genes identified for bacteremia. From these reads, PCR primers are developed.

Assay 2. Create RT-PCR primers to identify Staphylococcus aureus, Pseudomonas æruginosa, and Hæmophilus influenzae causing pneumonia. Using the reads generated from Assay 1, PCR primers are developed to identify pathogens causing hospital acquired pneumonia and applied to the sequenced samples and an independent cohort.

Expected results. The preliminary data show that patients in the ICU have bacterial RNA in the blood. There are a set of highly expressed genes from the bacteria during infection that can be the basis for identification. One outcome is that these genes differ from the genes expressed during bacteremia because some suggested that gene expression changes from the bacteria based on site of infection/colonization. The inventors have RNA sequencing data from patients with bacteremia and pneumonia due to similar pathogens and can see if different genes are expressed at higher rates. Primers can be developed for each pathogen based on site of infection to guide diagnosis. An alternative outcome is that the same target sequences are found in bacteremia and pneumonia. This would simplify product development on the NeuMoDx and require the test be integrated into other clinical diagnostics such as X-rays.

Potential alternatives. The technical approach is like EXAMPLE 1, which the inventors have shown is possible. See TABLE 4. Because the infection is in the lung, there may be no bacterial RNA identified in the blood of these patients, but other studies dispute this possibility. D'Mello et al., Proceedings of the National Academy of Sciences (Dec. 29, 2020). Bacterial DNA was detected in the blood of pneumonia patients. Langelier et al. (2020). The lung has a large surface area for gas exchange that would help with transfer of stable RNA or RNA in micro vesicles from the infection into the bloodstream. Blenkiron et al. (2016). Bacteremia complicates pneumonia in 6-17% of cases, depending on severity. Zhang, Yang, & Makam (2019). A subset of the pneumonia patients is expected to have target sequences shared with the patients studied in EXAMPLE 1. If the sequencing analysis cannot distinguish between the subgroups of pneumonia patients with and without bacteremia, clinical data are used to guide management.

Example 4

Validate these RT-qPCR Tests in Samples from Patients with and without Pneumonia (A2b).

Rationale. The PCR targets identified by sequencing can be used clinically. Hospital acquired pneumonia typically rapidly deteriorates a patient. RT-qPCR allows for the identification of pathogens, directly from blood, without culture, in less than four hours, and faster selection of pathogen-directed antibiotics. The goal is to eliminate invasive bronchoscopies, which can delay antibiotic administration and increase risks to the patient.

Assay 1. Test PCR primers on samples used for RNA sequencing. RNA sequencing cDNA libraries again are the initial test of the PCR primers. The cDNA from all samples with positive bronchial alveolar lavage cultures for each of the bacteria are used for this assay. Each cDNA sample are tested using the PCR primers for all pathogens. The inventors use cDNA from patients that had no hospital-acquired pneumonia.

Assay 2. Test PCR primers on samples that mimic collection for clinical use. This test is done obtain sensitivity and specificity in line with FDA requirements. PAXgene tubes from patients with and without confirmed hospital acquired pneumonia due to the pathogens of interest are identified. The PAXgene tubes are collected when bronchial alveolar lavage is collected. The patients' infection status is blinded to the researchers performing the PCRs. The stabilized RNA is extracted from the PAXgene tubes and globin and human rRNA are depleted using a commercial kit from New England Biolabs. cDNA is made with reverse transcriptase and then PCR are done with the primers. PAXgene tubes are used. RT-PCR are done immediately to ensure result in less than four hours.

Expected results. Specific RT-qPCR assays validate the sequencing and diagnose hospital-acquired pneumonia due to Staphylococcus aureus, Pseudomonas aeruginosa, and Hæmophilus influenzae from a direct from blood sample in fewer than four hours. Target abundance varies among patients (see, e.g., TABLE 4), which are correlated with severity of the pneumonia. Efforts are directed to finding primers that diagnose pneumonia and that are distinct from those for bacteremia despite being due to the same pathogen.

Example 5

Using the RNA from Patients with Infections, Design an RT-qPCR for the Most Common Resistance Genes Expressed that would Influence Treatment for Staphylococcus aureus, Escherichia coli, Pseudomonas æruginosa, and Hæmophilus influenzae (A3a).

Rationale. Delays in antibiotics worsen outcomes for all patients, including those with resistant organisms. Bonine et al. (2019). Overtreatment of organisms that do not carry resistance determinants worsens outcomes. Rhee et al., JAMA Network Open, 3(4), e202899 (Apr. 1, 2020). The goal of this EXAMPLE is to harness data from RNA sequencing to inform PCR-based diagnostics of antimicrobial resistance that are clinically relevant. Reads from the sequencing studies described above are aligned to a genome of resistance genes of interest, then PCR primers test against clinical specimens. These are phenotypic measurements of antibiotic resistance because gene expression and resistance phenotypes are closely linked. Suzuki, Horinouchi, & Furusawa (2014).

Assay 1. Assess the RNA Sequencing Data from Patients with Infections Due to Staphylococcus aureus, Escherichia Coli, and Pseudomonas æRuginosa, and H. influenza for Resistance Genes. a Genome is Made Using Clinically Relevant Resistance Genes.

For Staphylococcus aureus, the inventors include mecA (methicillin resistance) (Chambers & Deleo (2009); Guo et al. (2020)), qacA, norA, smr (efflux transporters of quinolones and tetracyclines) Guo et al. (2020), beta-lactamase (hydrolyses cefazolin) (Guo et al. (2020)), and VRSA (vanA, vanB, vanC, vanX, vanY, vanA). Escherichia coli targets include multiple beta-lactamases: basic beta-lactamases cleaving ampicillin, ESBL genes (TEM-1, TEM-2, and SHV-1) CTX-M (see TABLE 4), ampC, carbapenemases: KPC (class A), metallo (class B: IMP, VIM, NDM-1), OXA (class D) (Bajaj, Singh, & Virdi (2016), GyrA and ParC (fluoroquinolone resistance) (Tchesnokova et al. (2019), acrB (Karczmarczyk et al., 2011), ompF, Efflux pumps PabetaN, and qnr (Salah et al. (2019)).

For Pseudomonas æruginosa, see ampC, oprM, mexY (efflux transporters for quinolones and aminoglycosides) (Islam et al. (2009)), bla, gyrA, gyrB, parC (for quinolones) (Yang et al. (2015)), and aac(6′)-Ib,aphA1, and aadB (aminoglycosides) (Teixeira et al. (2016)).

For Hæmophilus influenzae, see TEM-1 and ROB-1. See Gutmann, Williamson, Collatz, & Acar, European Journal of Clinical Microbiology & Infectious Diseases: Official publication of the European Society of Clinical Microbiology, 7(5), 610-5 (1988), Tristram, Jacobs, & Appelbaum (2007)). Using these genes, PCR primers are identified based on RNA data from patients with these infections. This again are a primer for RT-PCR as the target are RNA. Using RNA as the target yields better results rather than DNA. This tool adapted for use with RNA data could enhance the phenotypic correlation using this data set. Bortolaia et al., The Journal of Antimicrobial Chemotherapy, 75(12), 3491-500 (2020).

Assay 2. Create RT-PCR primers to identify resistance Genes. Using the targets of interest from the deep RNA sequencing data, PCR primers cover resistance genes. Multiple primers may identify resistance genes for one pathogen. This are done through multiplexing that is possible with the machine from the industry partner. The target of these primers is the RNA in the blood. A reverse transcriptase is used to create the cDNA for which the primers interact. Targeting RNA has a better phenotypic correlation than targeting DNA from the pathogen because RNA signifies that the gene is being actively expressed.

TABLE 9
Antibiotic resistance markers and therapeutic considerations.

		Antibiotic if	Antibiotic if
Bacteria	Resistance Gene	present	absent	Ref.
Staphylococcus aureus	mecA	vancomycin	nafcillin	Chambers (1997)
Staphylococcus aureus	blaZ	nafcillin	cefazolin	Dingle et al. (2022)
Staphylococcus aureus	VRSA (vanA)	daptomycin	vancomycin
Escherichia coli	ESBL	carbapenem or	ampicillin or	Paterson et al. (2001);
		ceftolozane-tazo	3GenCeph	Rupp & Fey (2003)
Escherichia coli	Class A	ceftazidime-avi	ampicillin or	Rivera-Izquierdo et al.
	carbapenemase;	or aztreonam or	3GenCeph	(2021)
	Class B	tigecycline
	carbapenemase;
	Class D
	carbapenemase
Escherichia coli	gyrA, parC	ampicillin or	fluoroquinolone	Tchesnokova et al.
		cephalosporin		(2019)
Pseudomonas	ampC	carbapenem	cefepime/	Jacoby (2009)
aeruginosa			ceftazidime
Pseudomonas	oprM, mexY	non-	fluoroquinolone	Yang et al. (2015)
aeruginosa		fluoroquinolone
Pseudomonas	gyrA/parC point	non-	fluoroquinolone	Yang et al. (2015)
aeruginosa	mutation	fluoroquinolone
Haemophilus	tem-1, rob-1	amoxicillin-clav	amoxicillin	Gutmann, Williamson,
influenza				Collatz, & Acar (1988);
				Tristram, Jacobs, &
				Appelbaum (2007)
tazo = tazobactam,
avi = avibactam,
3GenCeph = 3rd generation cephalosporin,
clav = clavulanic acid.

Potential alternatives. In one detailed study of transcription and protein abundance in Escherichia coli, there was a lack of correlation between RNA and protein levels. Taniguchi et al. (2010). Though the overall abundances were not correlated, enzyme transcription and translation were closely correlated. Taniguchi et al. (2010). Some resistance phenotypes, such as fluoroquinolone resistance due to gyrA, gyrB, and parC, are mediated by SNPs. In this situation, the PCR primers are adapted for SNP detection such as the TaqMan assay. Easterday, Van Ert, Zanecki, & Keim (2005). For some resistance mechanisms, such as beta-lactamases, there are too many individual genes to test. In this situation, the inventors use k-mer analysis to identify primers capable of detecting entire classes of beta-lactamases. Marini et al. (2022). Ultimately, there are concerns for whether RNA-based detection of resistance is sufficiently comprehensive to be used in clinical practice. Regulatory RNA may play a role in resistance but not be detected by the sequencing approach. Dersch, Khan, Muhlen, & Görke (2017). In this situation, the inventors evaluate more patient specimens and alter sequencing protocols to detect unconventional RNAs.

For the assays described in EXAMPLE 5, blood from patients in the ICU with COVID-19 were collected during 2020 in Paxgene tubes. RNA sequencing was done as described by Fredericks et al., Science Reports, 12(1), 15755 (2022).

Example 6

Validate these PCR Tests for Resistance Genes in Samples from Patients with and without Infections (A3b).

Assay 1. Test PCR primers on samples used for RNA sequencing. cDNA libraries used for RNA sequencing are the initial test of the PCR primers. See FIG. 1. The cDNA from all samples with positive cultures with resistance are the positives. Each cDNA sample are tested using the PCR primers for all resistance genes to assess primer specificity.

Assay 2. Test PCR primers on samples that mimic collection for clinical use. These tests are done obtain sensitivity and specificity in line with FDA requirements. PAXgene tubes from patients with and without confirmed infections with resistance are used as negative controls. The assays include a positive control gene like actin to confirm PCR reaction in each specimen.

Expected results. Sets of PCR primers identified in this EXAMPLE detect resistance in these validation studies. The most straightforward tests are for the presence or absence of RNA encoding a resistance mechanism, such as mecA in MRSA. Although a molecular test is used in the clinical microbiology lab to diagnose MRSA, this test requires a positive blood culture bottle. The goal is to confirm an RNA-based blood test that alters treatment described in TABLE 5. That returns results in less than four hours without having to culture the patient's blood.

Potential alternatives. The principal concerns are for the level of target sequence found in blood, i.e., sensitivity, and the ability to identify primers that amplify the expected sequence, i.e., specificity. Strategies for improving sensitivity include using more cDNA in the PCR reaction and conducting a nested PCR. The inventors have not encountered evidence for inhibition of PCR reactions, which is due to the additional processing involved with using RNA as a PCR template. The inventors continue to use a positive reference gene, such as actin, to test for PCR inhibitors. There may be a large number of potential sequences that could convey a phenotype, such as the large family of beta-lactamases. k-mer analysis are used to identify sequences that represent the family, and primers are designed against that analysis. Modified PCR reactions, such as TaqMAMA, are used when mutations of pre-existing genes convey a resistant phenotype, as SNPs in gyrA and parC that are responsible for fluoroquinolone resistance. Another possibility is that important resistance mechanisms are infrequently encountered in the patient population, such as carbapenemase production. To create a more comprehensive test under those circumstances, the inventors can evaluate appropriate resistant strains in vitro, such as from the CDC & FDA Antibiotic Resistance Isolate Bank that is available to researchers. Another theoretical concern is that the test finds target sequences in patients without infections or normal controls. RT-qPCR holds a distinct advantage over endpoint PCR, so the inventors can establish a threshold cutoff for test positivity using relative abundance measurements of targets by the ΔCt calculation: Ct of assay −Ct of actin gene.

Example 7

Improved PCR primers for SARS-CoV-2 viremia Informed by RNA Sequencing.

COVID-19 is diagnosed using nucleic acid and antigen tests, of which the reverse transcription polymerase chain reaction (RT-PCR) is considered the gold standard. Peeling, Heymann, Teo, & Garcia, Lancet, 399(10326), 757-768 (2022). Although nasopharyngeal swabs are typically used for diagnostic testing, recent studies have shown SARS-CoV-2 viremia, or RNAemia. is correlated with disease severity and patient mortality. Fajnzylber et al., Nature Commun., 11(1), 5493 (2020), Heinrich et al., Open Forum Infect. Dis., 8(11), ofab509 (2021), Jacobs et al., Clin. Infect. Dis., 74(9), 1525-1533 (2022), and Rodriguez-Serrano et al., Science Reports, 11(1), 13134 (2021). Many patients in these studies did not show a detectable viremia, which is in part a reflection of disease severity. Improved sensitivity of a viral load measurement could provide more information about prognosis.

SARS-CoV-2 measurements typically use existing assays based on primers designed by the CDC, which were selected for specificity using in silico analyses. Lu et al., Emerg. Infect Dis., 26(8), 1654-65(2020). When these primers were created, there was little information available to inform their design beyond the SARS-CoV-2 sequence. Viral RNA sequences are unevenly represented in the bloodstream of patients with severe COVID-19. Lu et al., Emerg Infect Dis., 26(8), 1654-65(2020). Deep RNA sequencing showed two peaks were overrepresented in the alignments to the SARS-CoV-2 genome, suggesting that RT-PCR primers targeting those sites could detect viremia better. In this EXAMPLE, the inventors designed primers to measure the peak of the nucleocapsid (N) gene to be comparable to the widely used CDC-N1 primers (FIG. 2A) with similar GC percentages and amplicon length. BLAST analysis showed similar results as the CDC primers and no significant cross reactivity to other sequences, but they had different locations on the N-gene.

cDNA generated from RNA derived from the original sequenced cohort was first used to compare primers. See Fredericks et al., Science Reports, 12(1), 15755 (2022). Quantitative (q) RT-PCR reactions were performed. Ct values were compared using CDC-N1 primers as the reference. The N-peak primers were about 2-fold to 100-fold more sensitive than the CDC-N1 primers at detecting the N-gene (FIG. 2B).

Because of the inherent variability seen between patients and to validate the findings, viremia was tested in a second cohort of patients. Using a similar approach, the inventors found that N-peak primers were about 10-fold more sensitive than the CDC-N1 primers (FIG. 2C).

Enhanced sensitivity of RNAemia could improve the ROC curve and inform the lower range of viremia measurements. More information could improve assessments of prognosis, especially by those who may progress to develop more severe disease. The results of this EXAMPLE show the value of informing qRT-PCR primer design with RNA sequencing data because certain sequences or genes may be unexpectedly overrepresented during infection in vivo. Enhanced sensitivity could lead to diagnostics using direct-from-blood molecular testing.

RNA was isolated as described by Fredericks et al., Science Reports, 12(1), 15755 (2022). One hundred ng of total blood RNA, depleted of globin and ribosomal RNA, were used for cDNA synthesis with the SuperScript IV First-Strand Synthesis System (Invitrogen, USA) following the manufacturer's instructions in a final volume of 20 μL, and the cDNA was stored at −20° C. until use. Real-time qPCR was performed using iTaq Universal SYBR Green Supermix (Bio-Rad, USA) following the manufacturer's instructions. The final volume of each qPCR reaction was 10 μL, including 1 μL of cDNA, and the final concentration of the primers in each reaction was 400 nM. All qPCR reactions were centrifuged at 455 RCF for one minute before thermal cycling. The qPCR was performed using a CFX Connect or CFX96 instrument (Bio-Rad, USA) controlled by the CFX Maestro Software (Bio-Rad, USA) with the following thermal cycling protocol: 95° C. for thirty seconds and forty cycles of steps: (1) 95° C. for five seconds, (2) 60° C. for thirty seconds.

TABLE 7
Primer sequences used in this EXAMPLE

		Forward Primer	Reverse Primer
Name	Target Gene	Sequence 5′-3′	Sequence 5′-3′
N-Peak	SARS-CoV-2	ATGAAACTCAAGCCTTA	ATCCAAATCTGCAGCAG
	Nucleocapsid	CCGCA	GAAG
	gene	(SEQ ID NO.: 1)	(SEQ ID NO.: 2)
CDC-	SARS-CoV-2	GACCCCAAAATCAGCGA	TCTGGTTACTGCCAGTT
N1	Nucleocapsid	AAT	GAATCTG
	gene	(SEQ ID NO.: 3)	(SEQ ID NO.: 4)
ACTB	Human beta-actin	GCACCACACCTTCTACA	ATAGCACAGCCTGGATA
	gene	ATGAG	GCAAC
		(SEQ ID NO.: 5)	(SEQ ID NO.: 6)

Threshold cycle counts (Ct) were measured where beta-actin was the reference gene and the CDC-N1 primers were used as the calibrator.

TABLE 11
Calculations used in this EXAMPLE

Value	Calculation
Delta Ct (ΔCt)	ΔCt = Ct (N-Peak or CDC-N1) − Ct (ACTB)
Delta Delta Ct (ΔΔCt)	ΔΔCt = ΔCt (N-Peak) − ΔCt (CDC-N1)
Relative Fold Difference	relative fold difference = 2−(ΔΔCt)
Log2 Relative Fold	log2 relative fold difference = log2(2−(ΔΔCt))
Difference

Example 8

Stability and Motif Analysis of RNA-Seq Reads from COVID-19 Patients.

RNA sequencing has been increasingly incorporated in clinical diagnoses and management. See Ketkar, Burrage, & Lee, JAMA, 329(1), 85-86 (2023), Mortazavi et al., Nature Methods, 5(7), 621-8 (2008), and Peymani, Farzeen, & Prokisch, Pediatr. Investig., 6(1), 29-35 (2022). The technology has several clinical uses such as analyzing the transcriptome of a cancer and determining a type of infection. Huang, Wang, & Yao, Microb. Cell, 8(9), 208-222 (2021). RNA sequencing was used to elucidate the pathogenesis of certain diseases and potential treatment approaches. See Huang, Wang, & Yao, Microb. Cell, 8(9), 208-222 (2021). This laboratory technique can detect different transcript isoforms from alternative splicing, chimeric gene fusions, and other genetic changes. Mortazavi et al., Nature Methods, 5(7), 621-8 (2008). With alignment to pathogen organism genomes, comparisons between genetic expression of a pathogen can be made. Fredericks et al., Science Reports, 12(1), 15755 (2022).

Regulatory RNAs regulate metabolic and virulence functions of certain pathogens, showing the increasing pressure to expand the capability of RNA Sequencing to create a full transcriptome in the clinic. See Oliva, Sahr, & Buchrieser, FEMS Microbiol. Rev., 39(3), 331-49 (2015.), and Papenfort & Vogel, Front. Cell Infect. Microbiol., 4, 91 (2014). RNA is subject to multiple cellular processes that can affect genetic expression.

Up to 92-94% of human multiexon genes undergo alternative splicing. Houseley & Tollervey, Cell, 136(4), 763-76 (2009). Mutations in RNA modification enzymes were associated with over 100 human diseases. The estimated median mRNA half-life in humans is ten hours, with different functional groups of mRNA decaying at different rates. Yang et al., Genome Res, 2003. 13(8), 1863-72.

The stability of mRNA alters gene expression and mRNA life span. RNA viruses evade degradation by maintaining mRNA stability. See Houseley & Tollervey, Cell, 136(4), 763-76 (2009), and Moon, Barnhart, & Wilusz, Curr. Opin. Microbiol., 15(4), 500-5 (2012). Stability of RNA was measured by the minimum free energy (MFE) of the structure and the ensemble free energy (EFE) of the structure. See, Ding, Chan, & Lawrence, RNA, 11(8), 1157-66 (2005), Doshi et al., BMC Bioinformatics, 5, 105 (2004), Wuchty et al., Biopolymers, 49(2), 145-65 (1999), Lorenz et al., ViennaRNA Package 2.0. Algorithms Mol. Biol., 6, 26 (2011), Trotta, PLoS One, 9(11), e113380 (2014), and Vasudevan& Steitz, Cell, 128(6), 1105-18 (2007).

This EXAMPLE presents an analysis of the stability of RNA-Seq reads from COVID-19 infection patients. The inventors established RNA motifs that either increase or decrease stability of the RNA-Seq read fragment. The inventors assessed whether the destabilizing RNA motif affects duplicate RNA-Seq reads.

Results. Of the 676 reads from RNA-Seq, there were 137 unique sequences. Thus, 539 reads were identical with another read. Among all the unique read sequences, the average minimum free energy (MFE) in kcal/mol was −30.46 and the average ensemble free energy (EFE) in kcal/mol was −32.94. Of the repeated sequences, there was on sequence that was repeated 328 times. The minimum free energy for this sequence was −33.00 kcal/mol and the ensemble free energy was −35.20 kcal/mol. Despite being highly repetitive, it was only the 48^th lowest MFE and the 60^th lowest EFE.

For the analysis of the minimum free energy and ensemble free energy values of read sequences in whole gene regions, read sequences were found in six genes, the nucleocapsid (N) gene, the ORF1ab gene, the ORF3a gene, ORF6 gene, ORF8 gene, and the spike s gene. The N gene and S gene encode integral structural proteins and the ORF3a, ORF6, and ORF8 genes encode auxiliary genes. The ORF1 ab genes encode other nonstructural proteins.

A Welch's ANOVA analysis showed there was at least one mean minimum free energy for a gene that was significantly different from another gene's mean minimum free energy (p=0.0004907). The post hoc analysis assessed fifteen pairs among the six genes and showed three significant relationships. The mean minimum free energy of the N gene was significantly different from that of the ORF1 ab gene (p=2.81e−7). The mean MFE of the N gene was significantly different from that of the ORF6 gene (p=p=0.23). The ORF6 gene's mean minimum free energy differed significantly from the S gene's mean minimum free energy (p=0.037). See schematics in FIG. 3 and FIG. 4,

For ensemble free energy, the Welch's ANOVA analysis showed there was at least one mean ensemble free energy for a gene that differed significantly from another gene's mean ensemble free energy (p=0.002398). The post hoc analysis found four significant pairwise comparisons. The mean ensemble free energy of the N gene differed significantly from that of the ORF1 ab gene (p=0.005). The mean ensemble free energy of the N gene was significantly different from that of the ORF6 gene (p=0.03). The mean ensemble free energy of the ORF3a gene differed significantly from that of the ORF6 gene (p=0.027). The ORF6 gene's mean minimum free energy differed significantly from the S gene's mean ensemble free energy (p=0.036). For the analysis of the minimum free energy and ensemble free energy values within the N gene, a Welch's ANOVA analysis was completed. Later, a Welch's t-test was completed comparing each of the gene groups individually to each other.

The motif analysis using MEME-ChIP of all the read sequences discovered ten motifs, of which six had known or similar motifs in the database. See sequences in FIG. 6. Three of these motifs have a higher minimum free energy for the read sequences (p=0.00502, p=0.00000422, p=0.00023) and a higher ensemble free energy for the read sequences (p=0.0034, p=0.0000034, p=0.00039). The motif analysis using XSTREME of all the read sequences discovered thirty-four motifs. See FIGS. 7A-7I. For six of the identified motifs, sequences that had the motifs have significantly different minimum free energy values compared to sequences without the motif (p=0.0275, p=0.0204, p=0.000455, p=0.0082, p=0.00175, p=3.26e−79). These motifs were labeled MEME-9, MEME-10, MEME-21, MEME-22, MEME-27, and MEME-28. For all these motifs besides MEME-10, sequences with the motifs have significantly different ensemble free energy values compared to sequences without the motif (p=0.0315, p=0.000664, p=0.00695, p=0.00114, p=0.000328). A negative binomial regression was performed to assess if MEME-28, the only motif found to be stabilizing, was associated with sequences with a higher number of duplicates. This analysis was not significant with a p value of 0.266.

Discussion This initial chi-squared goodness of fit test showed the proportion of reads from different genes were not equal. RNA-Seq does not uniformly collect reads from each gene. There may be factors that influence a particular sequence being detected. One known factor is RNA expression. With different levels of gene expression for different genes, the large number of certain RNA sequences may be greater than other sequences at different time points of a cell.

Degradation and stability may be other factors that play a role in detection by an RNA-Seq assay. When PCR primers are designed based on RNA sequencing data, stability of structure should be included not just in design but also optimization of the work flow.

The results of his EXAMPLE showed the stability of reads from different genes varied. Because genes like ORF6 had sequences that were less stable compared to genes like the S gene, ORF6 may be underrepresented in the RNA-Seq analysis. If true, there will be a significant impact on our interpretation of RNA-Seq results. Genes that may be regarded as lowly expressed and disregarded to focus on seemingly highly expressed genes may be new targets to be reanalyzed. The contributions of these genes to cellular function may have been underestimated.

Within the N gene, reads from different regions differed in stability. Because all these reads came from the N gene, differences in the quantity of duplicates did not arise from the expression of the N gene itself but the abundance of potentially alternatively spliced RNA and stability of RNA fragments. One sequence in the N gene was heavily duplicated with 328 repeats.

The motif analysis of this EXAMPLE showed motifs that corresponded to destabilization of the RNA and a single motif that corresponded to stabilization of the RNA. These motifs were not present in the most duplicated read sequence, but there may still be motifs in that read sequence that were not detected by our analysis. The most repeated sequence may have motifs that confer increased stability or an increased chance to be detected by RNA-Seq, but it was not discovered.

The motif analysis in this EXAMPLE used both the established motif analysis tool MEME-ChIP and the new motif analysis tool XSTREME. MEME-ChIP is optimized for sequences larger than our average sequence length. MEME-ChIP discovered three of the eight motifs that affected RNA stability that XSTREME could not. These two motif analysis tools discovered different motifs. Both should be used for later assays.

This EXAMPLE provides the discovery of new motifs that may confer increased or decreased stability for RNA. Using the motifs discovered to alter stability while limiting differing gene expression levels and alternative splicing, the relationship between stability and RNA-Seq read duplications can be further elucidated, allowing for further analysis, and broadening of research implications of the already well-known RNA-Seq experiment.

Example 9

Deep RNA Sequencing and Aligned Unmapped Reads from Patients to a Custom Genome Constructed and Retrieved from the NCBI Gene Database.

Whole blood samples were collected from patients in the ICU, stored in PAXgene tubes to preserve the integrity of the specimens, and submitted for RNA sequencing by a commercial sequencing service (Azenta/Genewiz).

In this EXAMPLE, the inventors used deep RNA sequencing and aligned unmapped reads from patients to a custom genome constructed and retrieved from the NCBI Gene database. See TABLE 5 in EXAMPLE 1.

All analyses were conducted blinded to clinical data and patient outcomes. Unmapped RNA sequencing reads were aligned to all four custom genomes using the STAR aligner for classification, extraction and count of unmapped reads.

Four custom genomes were created and aligned against the unmapped reads retrieved from the patient. When a read of at least 100 base pairs aligned to the pathogen genome, it was counted as a read a plotted to the pathogen genome. Density plots (see FIG. 8) were then used to identify the areas of the genomes with the most reads. When compared to clinical microbiology data like positive cultures and clinical outcomes like mortality, targets from the pathogen genome were identified as targets to base PCR based tests upon.

Specifically for Escherichia coli. Reads from patients with positive blood cultures were grouped together and compared to reads that occurred from patients that were found to not have an Escherichia coli infection. FIG. 9 shows the reads from the two groups. When comparing there are significantly more reads in the patients with positive Escherichia coli infections. Using these plots target genes to design PCR tests on clinical microbiology machines.

Specifically for Escherichia coli, the following genes and exact nucleic acid targets will be used based RNA sequencing data. Both ribosomal RNA and mRNA targets will be used. Final targets are blasted against all known genomes to ensure no false signals.

• • rrIE Ribosomal RNA 50S subunit
  • Coordinates NC_000913.3:4210165-4210194
  • IdtD Murein L,D-transpeptidase
  • Coordinates NC_000913.3:982647-982657
  • pitB Phosphate Transporter
  • Coordinates NC_000913.3:3135153-3135165

These three sites were chosen for several reasons including increases in numbers seen in patients with Escherichia coli infections, the target of mRNA because this last five-eight minutes and ensuring that the targets are unique only for this pathogen. Identification of these targets with methods such as PCR or nucleic acid probes will identify this as the causative pathogen and change treatment to an appropriate antibiotic.

The work done to identify these targets can be repeated on the other pathogens noted and for all the resistance genes.

Example 10

RNA Sequencing of Sepsis Patients Informs Tests to Diagnose Resistant Pathogens and Resistance and Alter Treatment

Introduction. Traditional methods for diagnosing infections and determining antibiotic resistance have typically involved culturing samples from patients to identify the causative pathogen and then performing susceptibility testing to determine the appropriate antibiotic treatment. These culture-based methods are time-consuming and can take days to yield results, delaying the initiation of targeted therapy and potentially leading to adverse patient outcomes. These methods may not always accurately reflect the true microbial composition of the infection due to limitations in culturing techniques.

Regarding critical diagnosis of infections, sepsis is a life-threatening condition that arises when the body's response to infection causes injury to its own tissues and organs. It is a major cause of morbidity and mortality worldwide, necessitating rapid and accurate diagnosis to improve patient outcomes. The traditional diagnostic methods, such as taken from blood cultures, can take several days to identify the causative pathogen and determine its antibiotic resistance profile. This delay can lead to the use of broad-spectrum antibiotics. which may contribute to antibiotic resistance and negatively impact patient care.

Molecular microbiology has advanced significantly, offering faster and more precise diagnostic tools. Current molecular tests primarily focus on DNA to identify pathogens and their resistance genes. These methods still have limitations in terms of speed and comprehensiveness RNA sequencing has emerged as a powerful tool in genomics, providing insights into gene expression and the presence of pathogens in a sample. What is needed are new methods that can leverage RNA sequencing in diagnosing pathogens.

By leveraging RNA sequencing, it is possible to obtain a more detailed and timely understanding of infectious agents and their resistance mechanisms, transforming the approach to diagnosing and treating sepsis.

In recent years, molecular diagnostic techniques such as polymerase chain reaction (PCR) were used for the rapid and sensitive detection of pathogens in clinical samples. PCR-based assays can target specific genetic sequences unique to pathogens, allowing for the direct detection of microbial DNA or RNA in patient samples. While PCR assays have improved the speed and accuracy of pathogen detection, they have been focused on identifying the presence of pathogens rather than assessing antibiotic resistance profiles.

Efforts to address the challenge of antibiotic resistance in clinical settings involve the development of molecular methods that can detect specific resistance genes carried by pathogens. These methods typically rely on targeted amplification of resistance gene sequences using PCR primers designed to selectively amplify the genetic markers associated with antibiotic resistance. By identifying these resistance genes, clinicians can make informed decisions regarding appropriate antibiotic therapy for infected patients. Existing approaches often require separate assays for pathogen detection and resistance profiling, leading to increased complexity and turnaround times in diagnostic workflows. None of these approaches have provided a comprehensive solution that combines the features described in this disclosure.

The present invention discloses a method for diagnosing infections and antibiotic resistance by performing RNA sequencing on a blood (or other) sample from a patient with sepsis. Pathogen RNA targets and resistance genes not mapping to the human genome are identified from the sequencing data. PCR primers are designed based on the identified targets and genes and used in a PCR test to diagnose the infection and determine antibiotic resistance in the patient. This innovative approach enables accurate and efficient diagnosis of infections and antibiotic resistance, providing valuable insights for personalized treatment strategies in sepsis patients.

The technology disclosed herein shows RNA sequencing can identify pathogen targets for new PCR tests that change treatment for resistant bacterial pathogens. In this cohort study of patients with sepsis, RNA sequences were identified for pathogens and resistance genes that correlated with laboratory and clinical findings. With this information, new PCR tests can be designed toward clinically relevant resistant pathogens that would change treatment and improve outcomes.

Importance. Diagnosis of infection in patients with sepsis takes days via culture and appropriate treatment of resistant pathogens are delayed awaiting these results. A faster diagnosis of the pathogen and resistance via RNA sequencing informed PCR will improve outcomes.

Objective. Use RNA sequencing from patients with sepsis to identify targets for future nucleic acid based tests.

Design. Cohort study of 46 sepsis patients admitted to the intensive care unit with samples taken on days 0, 1, 3, and 7 with follow up through the hospital stay during 2021-2022. All patients had RNA sequencing (depth of >100 million reads) done on days they were in the ICU.

Setting. Single center medical intensive care unit.

Participants. Patients were admitted to the intensive care unit with a diagnosis of sepsis. Patients or surrogates were approached consecutively and those who consented were enrolled.

Outcomes and measures. RNA sequencing of peripheral blood was done to identify pathogen RNA targets. RNA sequencing data that did not map to the human genome was then aligned to resistance genes and pathogen genomes and used to design PCR tests. This test was correlated to culture diagnosis and clinical outcomes.

Results. Forty-six patients (mean age 62.2, 48% female) were enrolled and samples from 87 time points were collected. These samples resulted in 8.6 billion RNA sequencing reads to identify pathogen RNA. Nucleic acid target design for this study focused on positive blood cultures (forty) due Escherichia coli (five), Staphylococcus aureus (six), and Pseudomonas æruginosa (three) out of a total 90 sent from these patients as well as identification of resistance genes. From these RNA sequencing reads, forty targets were designed and tested with PCR. In cohort of patients (n=9) the some of the proposed PCRs identified all cases of positive blood cultures (PA0668.4-2 for Pseudomonas æruginosa and SAOUSHC-R0001,2 for Staphylococcus aureus, Escherichia coli had no positive blood cultures in this cohort).

Conclusions and relevance. RNA sequencing from patients with sepsis can identify RNA from pathogens causing the infection. This RNA can be used to design PCR primers that can identify patients with positive blood cultures. Translation of these primers to clinical microbiology machines is the next step and will be clinically relevant to diagnose pathogen and resistance faster than blood culture.

Example 11

Sepsis

Sepsis is responsible for one out of five deaths in the world. Rudd et al., Lancet (London, England), 395(10219), 200-11 (2020). Sepsis is defined by altered physiology leading to organ and immune system dysfunction due to an infection Singer et al., The Third International Consensus definitions for sepsis and septic shock (Sepsis-3). JAMA, 315(8), 801-10 (February 2016). The care of sepsis is multifactorial with focus on early antimicrobial treatment, fluid resuscitation, and support with vasoactive medications. Rhodes et al., Surviving Sepsis campaign: International guidelines for management of sepsis and septic shock: 2016. Intensive Care Medicine. 43(3), 304-377 (March 2017). Data from before the COVID-19 pandemic states that in the United States 1.7 million patients have sepsis. This leads to 270,000 deaths. Rhee et al., JAMA, 318(13), 1241-1249 (Oct. 3, 2017). The diagnosis of the infection was stated as a prominent challenge to sepsis care. Duncan, Youngstein, Kirrane, & Lonsdale, Current Infectious Disease Reports, 23(12), 22 (2021). This project proposes to better diagnose bacterial infections and associated antimicrobial resistance in order to improve outcomes. The Surviving Sepsis Campaign has standardized treatment for sepsis that includes blood cultures before broad-spectrum antibiotics and initiation of antibiotics within one hour. Evans et al., Critical Care Medicine, 49(11), e1063-e1143 (Nov. 1, 2021). In multivariate analysis of factors impacting mortality in patients with septic shock, time to initiation of antibiotics was the most impactful variable, exemplified by 79.9% survival in septic shock patients with antibiotics in the first hour and a reduction in survival by 7.6% for every hour delay. Kumar et al., Critical Care Medicine, 34(6), 1589-96 (June 2006). The importance of appropriate antibiotics is exemplified by the fact that treating five patients saves one life. Vazquez-Guillam et al., Critical Care Medicine, 42(11), 2342-9 (November 2014). Sepsis not diagnosed on admission has larger costs and utilization burden. Paoli. It is important to obtain samples to diagnose the pathogens early and quickly.

Antimicrobial resistance is associated with approximately five million deaths each year in the world. Rudd et al., Lancet (London, England), 395(10219), 200-11 (2020). Infections in the ICU with resistant bacteria have a higher risk of in-hospital death. Vincent et al., JAMA, 323(15), 1478-1487 (Apr. 21, 2020). This is exemplified by resistant Escherichia coli associated with 16.5 deaths per 100,000 in Europe. Mestrovic et al., The Lancet Public Health (Oct. 13, 2022). Typical treatment of sepsis begins with broad-spectrum antibiotics that are deescalated once the pathogen and microbial sensitivities are determined. If a patient has a resistant pathogen, this initial treatment is not effective and does not change until the cultures are final, delaying appropriate treatment for days. Broad-spectrum antibiotics, although appropriate initially, do have an adjusted increased risk of mortality. Webb et al., The European Respiratory Journal, 54(1) (2019). Other studies specifically state that unnecessary, i.e., no resistant isolates identified, use of broad-spectrum antibiotics results in an increased risk of in-hospital deaths. Rhee et al., JAMA Network Open, 3(4), e202899 (Apr. 1, 2020). The proposed test would limit broad-spectrum antibiotics.

In the initial assessment of RNA sequencing data, the reads are aligned to the genome of the species the sample came from. The unmapped reads can account for up to 5-20% of the data and this data is typically discarded. In our mouse models and with humans with critical illness, there are more unmapped reads (˜30%). Fredericks et al., Scientific Reports, 12(1), 15755 (Sep. 21, 2022), Fredericks et al., Intensive Care Medicine (Feb. 20, 2020). identification of bacteria RNA known to cause the infection (based upon culture data) from the RNA sequencing data can guide the production of better diagnostic tests. It is important to note that the proposed nucleic acid target is RNA, when other current molecular microbiology tests look at DNA. The RNA that is identified in the blood of patients with sepsis is either RNA from a highly expressed gene or RNA that has a unique structural property that inhibits degradation. Regardless, the RNA that is identified from each pathogen with reads across multiple patients who are critically ill with that infection will become the target for the PCR test. This new PCR test will allow for faster diagnostic compared to the gold standard of blood culture. Target primers to resistance genes can be developed that are identified in the blood by RNA sequencing.

Methods. This is a single center, prospective study of critically ill patients with sepsis and their pathogen profile. The patients were enrolled from Rhode Island Hospital Medical Intensive Care Unit (MICU), all of whom or their appropriate surrogate provided informed consent as approved by the Rhode Island Hospital Institutional Review Board (411616, 205821). Clinical data was collected as previously described by Monaghan et al., Molecular Medicine {Cambridge, Mass), 24(1), 32 (Jun. 18, 2018). Whole blood samples were drawn in PAXgene tubes (Qiagen, Germantown, MD) to immediately stabilize the RNA and then sent to Genewiz (South Plainfield, NJ) for RNA extraction, human ribosomal RNA depletion and deep RNA sequencing. The inventors did not select for polyadenylic acid (poly A) tails to include non-protein coding RNAs. The sequencing was performed on Illumina HiSeq machines to provide 150 base pair, paired-end reads and at least 100 million reads per sample. Raw sequencing data was returned on secure external hard drives. Fredericks et al., Science Reports, 12(1), 15755 (2022).

The raw data was assessed for quality control with FastQC then aligned to the most recent assembly (GCF_000001405.40) of Genome Reference Consortium Human Build 38 (GRCh38)—the latest version of human reference genome—with the STAR aligner. Dobin et al., Bioinformatics, 29(1), 15-21 (January 2013), Schneider et al., Genome Res., 27(5), 849-864 (May 2017), Andrews, FastQC: A quality control tool for high throughput sequence data (2014). Reads that aligned to GRCh38 (mapped reads) were separated from unmapped reads. The unmapped reads representing non-human genome were then aligned to each reference genome of twenty-eight pathogens (eTABLE 1) and an anti-microbial resistance (AMR) custom genome selected based on clinical utility. The pathogen reference genome was obtained from NCBI RefSeq database. The AMR custom genome was created by concatenating twenty-five common resistance genes in pathogens (eTABLE 2). After mapping the unmapped reads to each pathogen, the most clinically relevant pathogens as defined by positive blood culture (Escherichia coli, Pseudomonas æruginosa, and Staphylococcus aureus) and AMR genes were selected for primer design.

For each pathogen, the reads of each patient sample with known bacteremia, as defined by positive blood culture, were compared to discover common sequences at least 100 base pairs (bp) long. This minimum bp criterion was chosen to provide sufficient length for primer design. Each 150 bp read from each sample was searched in other samples with each pathogen-specific bacteremia. If a common 150 bp sequence was found in all bacteremia samples, then this sequence was selected as a primer target based on the following criteria: at least 85-90% hits unique to the specific pathogen on NCBI genome blast, a few to no hits for other samples without bacteremia, and no hits for samples mapped to other pathogens. These criteria were established to exclude common sequences that aligned to related pathogens, e.g. Escherichia coli and Shigella species, and to include sequences that are unique to sepsis. If there were no common sequences 150 bp long, then the search with shorter bp, e.g. 145, 140, or 135, continued until there was a match among all samples. If there was no match above 100 bp among all samples with bacteremia, then sequences above 100 bp common to at least a few bacteremia samples were searched. If none were found, then a read that meets the aforementioned criteria from each sample was chosen. For the AMR genes, the same process was performed except the sequences did not require to have 85-90% hits unique to a specific pathogen, as AMR genes can be found in multiple pathogens. These computational analyses were performed on RStudio and command line using bash, awk, and grep scripts.

Primers were designed to the targets described above. PCR primer sets were designed using NCBI's Primer-BLAST online software. Sequences derived from RNA-seq data were uploaded, specificity checking parameters were adjusted to Refseq RNA, and internal probe output was requested. Primer sets were chosen to have similar amplicon size while minimizing self-complementarity. They were ordered from IDT and tested against nucleic acids recovered from a small sample of sepsis patients that had RNA available for testing. The reverse transcription of RNA to cDNA was done with 100 ng RNA input using the Superscript IV First Strand Synthesis System Kit (ThermoFisher/Invitrogen, Waltham, MA). The cDNA was then used as template in the qPCR reaction and carried out with the Launa Universal Probe qPCR Master Mix kit (New England Biolabs, Ipswich, MA) and the custom primers/probes (IDT Coralville, IA). The qPCR reaction was setup at 1 μl of Primer/probe(primer 400 nM/probe 200 nM), 1 μl of Template and 10 μl of the Master mix. The amplification was run on qPCR instrument (BIO RAD CFX96 Real-Time System) with the thermocycling condition for forty cycles. Reactions were done in pairs with no reverse transcription controls. Positive results from the PCR were defined as greater than two cycle counts over the no reverse transcription controls or less than forty cycle counts if the no reverse transcription controls were negative.

Patients. Forty six patients enrolled in the study (TABLE 1), 87 distinct time point samples were collected while the patients were in the ICU. This resulted in over 8.6 billion data points from RNA sequencing. The workflow for the PCR development is described in FIG. 10. In this cohort, 228 cultures were sent and 114 were positive with ninety being from the blood and having forty positives. The most commonly positive pathogens for blood culture in this cohort were then used to create PCR targets: Escherichia coli (five), Pseudomonas æruginosa (three), and Staphylococcus aureus (six).

Alignment to pathogens/resistance genes and target creation: Removing the RNA sequencing reads that mapped to the human genome left XXX unmapped reads of which ˜1.2 million aligned to a pathogen (eTABLE 3). It is important to note that since all the unmapped reads are aligned to each pathogen genome, a single RNA read may map to multiple pathogens. Sequencing data from patients with positive blood cultures for Escherichia coli (five), Pseudomonas æruginosa (three), and Staphylococcus aureus (six) was then used to identify unique regions of the genomes that were only present in the data of patients with active infections (TABLE 2). As seen in TABLE 2 and eTABLE 3, simply using the number of reads as a diagnostic tool is not adequate. In patients with positive blood cultures nucleic acid sequences only found in those patients with that specific infection were identified and created the PCR primer targets (eTABLE 4, eTABLE 5, and eTABLE 6). These TABLES list the gene to which those nucleic acid sequences align.

Many fewer reads align to specific resistance genes, 46 total (FIG. 10). From these reads, targets for PCR were made for six genes. There were eleven regions across these genes that were targets, specifically four targets for TEM-1 and three targets for TEM-2. eTABLE 7 shows the primers and the gene it will identify.

Results of PCR in patient samples. A subset (n=9) of the patients initially included in the RNA sequencing analysis had samples tested by PCR (eTABLE 8). Three of the patients had Staphylococcus aureus bacteremia, one had Pseudomonas aeruginosa bacteremia, and none had Escherichia coli bacteremia (TABLE 3). One target for Staphylococcus aureus, SAOUSHC-00439, had a sensitivity of 33 and specificity of 100 while another, SAOUSHC-R0001,2, had a sensitivity of 100 and specificity of 66. For Pseudomonas æruginosa, the target PA0668.4-2 had 100 sensitivity and specificity. One patient had an Escherichia coli urinary tract infection and was the only patient with a PCR positive for yegP. Because there were no samples for patients with Escherichia coli bacteremia, negative and positive results in this cohort can provide some insights. Because every patient was positive for rrlABCEH, rrlDG, and rrlD these would not be good targets to identify Escherichia coli bacteremia, ernrB, rapA-1, yghQ, gdx were all negative and still could be used to identify an active infection with Escherichia coli. For Pseudomonas æruginosa PA0668.4-1 and PA4280.2 are not good targets because they were positive in many samples but rpoS, oprN, and ahpF could be used in combination with PA0668.4-2 to identify positive blood stream infections. Two primers, SAOUSHC-R0009,10 and SAOUSHC-R0006,7 are not good targets for Staphylococcus aureus due to many false positives. The target SAOUSHC-R0008,9 provides an interesting result as the cycle counts needed for a patient with bacteremia are lower than other false positives. There was limited antimicrobial resistance identified by culture in this patient cohort. Because there were no positive results targets for Mex-MexY, ESBL CTX-M-223, ornpF could still be useful. There are many positive results for TEM-1 and TEM-2. The only patient with a positive PCR for NDM-1 had no resistance identified clinically but did die in the hospital and was not receiving appropriate antibiotics for an infection expressing this gene.

Discussion: Diagnosis of infections causing sepsis needs a faster solution so appropriate antibiotics can be administered and reduce mortality. Utilizing PCR tests targeting RNA from pathogens, informed from RNA sequencing data from patients with sepsis can identify bacteria and potential resistance directly from the blood, without the need for culture in approximately four hours (FIG. 10). This works shows that RNA sequencing data from patients (TABLE 2) with positive cultures can design primers for Escherichia coli (eTABLE 4), Pseudomonas æruginosa (eTABLE 5), and Staphylococcus aureus (eTABLE 6) that show promising results in a small cohort of patients (TABLE 3).

Many molecular diagnostics for infectious disease focus on DNA. Ahmad et al., Cureus. 16(6), e61476 (June 2024). This work focuses on RNA from pathogens as identified from RNA sequencing data (eTABLE 3) of patients with sepsis. Because RNA is the focus, stabilization of RNA at collection is paramount for the success of this technique (use of PAXgene tubes). RNA as the target allows for the PCR to occur directly from the blood without the need to culture to increase the amount of pathogen DNA available because the RNA target is actively being transcribed. The half-life of most bacterial mRNA is less than an hour. If detected, it is corning from an active infection. Baumeister et al., Nucleic Acids Research, 19(17), 4595-600 (Sep. 11, 1991). If RNA is detected, the genes (particularly resistance genes) is being expressed.

The use of PCR in these results shows that it is more sensitive than RNA sequencing alone. All the PCR primers were designed from patients with positive blood cultures. The targets were then checked to ensure that they were not present in any of the samples without positive blood cultures and against the genomes of all the pathogens studied. Some of the targets (eTABLE 8) were positive in all the samples. This increased sensitivity is important because the RNA from the pathogen and resistance genes will be rare in the samples.

Identification of resistance is key to changing the treatment of patients with sepsis. Some research suggests using sequencing to identify resistance in clinical isolates, still requiring culture. Sauerborn et al., Nature Commun., 15(1), 5494 (Jun. 28, 2024). Current techniques to use PCR on DNA targets does not identify a living cell or expression of that gene, so use with antimicrobial resistance is limited. Ahmad et al., Cureus. 16(6), e61476 (June 2024). Identifying RNA produced from the resistance gene suggests that the pathogen is producing the protein. Heteroresistance is a clinically significant problem and can explain why some patients have no resistance detected but do not respond to antibiotics and have poor clinical outcomes. Wang et al., Infection (Aug. 14, 2024). In the sample of patients only two had any resistance detected by culture, but one patient had a positive PCR for NDM-1 (TABLE 3). The identification of this resistance in this patient could have occurred on day 0 of their hospital stay and antibiotics could have been given to prevent this death. This PCR for resistance genes could be done daily to identify patients treated with culture supported efficacy that fail due to induction of resistance genes in the pathogen. Mounier et al., Ann Intensive Care, 12(1), 107 (Nov. 17, 2022).

In the initial data, questions arise as to why RNA from many pathogens is there detected in the blood. There is much research on the blood microbiome. Cheng et al., International Journal of Molecular Sciences. 24(6) (Mar. 15, 2023). So as to not bias the results, all un-mapped reads were aligned to each individual pathogen. A read could map to multiple pathogens when they are genetically similar. Fukushima, Kakinuma, & Kawaguchi, Journal of clinical microbiology. 40(8), 2779-85 (August 2002). To create primers unique for the pathogen, this project uses samples from Escherichia coli (five), Pseudomonas æruginosa (three), and Staphylococcus aureus (six) positive blood stream infections but only tests on a sample of nine patients. Future testing of these targets on more samples, particularly those not used to design the primers, is needed and planned. Because the target is RNA, these samples must be collected in a way that the RNA is immediately stabilized. More samples allow for expansion of the pathogens and resistance genes targeted. In order to design a primer for a pathogen, patients with positive infections will need to be sequenced to allow for identification of unique RNA. Patients with resistance will need to be sequenced as well, particularly for common pathogens like MRSA. No patients in this cohort had MRSA bacteremia.

The focus was on patients with bacteremia, but sepsis can be due to infections anywhere in the body. Future work will need to cluster the patients based on site and previous work suggests that the targets for a specific pathogen will differ based upon the site of infection. D'Mello et al., Proceedings of the National Academy of Sciences (Dec. 29, 2020). Translation of the workflow from RNA isolation to the PCR reactions done on research machines in this study to machines approved for in-vitro diagnostics will need to be done. There is a potential to use digital PCR (dPCR) as it would be more sensitive as well as the ability to quantify levels and monitor treatment overtime.

In some embodiments, the techniques described herein relate to a method for diagnosing infections and antibiotic resistance, including performing RNA sequencing on a blood sample from a patient with sepsis, identifying pathogen RNA targets and resistance genes from the RNA sequencing data that do not map to the human genome, designing PCR primers based on the identified pathogen RNA targets and resistance genes, and using the PCR primers in a PCR test to diagnose the infection and determine antibiotic resistance in the patient. The techniques described herein relate to a method, wherein the RNA sequencing is performed on peripheral blood from the patient. The techniques described herein relate to a method, wherein the PCR test diagnoses the infection and determines antibiotic resistance faster than culture-based methods. The techniques described herein relate to a method, further including aligning the RNA sequencing data to resistance gene and pathogen genome databases to identify the pathogen RNA targets and resistance genes.

In some embodiments, the techniques described herein relate to a method, wherein the PCR primers are designed to specifically identify the pathogen causing the infection and any antibiotic resistance genes present. The techniques described herein relate to a method, wherein the PCR test provides a rapid diagnosis of the infection and antibiotic resistance to guide treatment decisions.

In some embodiments, the techniques described herein relate to a method, wherein the method allows for limiting the use of broad-spectrum antibiotics by providing a targeted diagnosis. The techniques described herein relate to a method, wherein the patient is diagnosed with sepsis before performing the RNA sequencing.

In some embodiments, the techniques described herein relate to a method, wherein the blood sample is obtained within twenty-four hours of the patient being diagnosed with sepsis. The techniques described herein relate to a method, wherein the PCR test is performed within six hours of obtaining the blood sample.

In some embodiments, the techniques described herein relate to a system for diagnosing infections and antibiotic resistance, including: an RNA sequencing apparatus configured to perform RNA sequencing on a blood sample from a patient with sepsis, a computing device configured to: identify pathogen RNA targets and resistance genes from the RNA sequencing data that do not map to the human genome, and design PCR primers based on the identified pathogen RNA targets and resistance genes, and a PCR apparatus configured to use the PCR primers in a PCR test to diagnose the infection and determine antibiotic resistance in the patient. The techniques described herein relate to a system, wherein the RNA sequencing apparatus is configured to perform the RNA sequencing on peripheral blood from the patient.

In some embodiments, the techniques described herein relate to a system, wherein the PCR apparatus is configured to diagnose the infection and determine antibiotic resistance faster than culture-based methods. The techniques described herein relate to a system, wherein the computing device is further configured to align the RNA sequencing data to resistance gene and pathogen genome databases to identify the pathogen RNA targets and resistance genes.

In some embodiments, the techniques described herein relate to a system, wherein the computing device is configured to design the PCR primers to specifically identify the pathogen causing the infection and any antibiotic resistance genes present. The techniques described herein relate to a system, wherein the PCR test performed by the PCR apparatus provides a rapid diagnosis of the infection and antibiotic resistance to guide treatment decisions.

In some embodiments, the techniques described herein relate to a system, wherein the system allows for limiting the use of broad-spectrum antibiotics by providing a targeted diagnosis. The techniques described herein relate to a system, wherein the blood sample is obtained from a patient diagnosed with sepsis.

In some embodiments, the techniques described herein relate to a system, wherein the blood sample is obtained within twenty-four hours of the patient being diagnosed with sepsis. The techniques described herein relate to a system, wherein the PCR apparatus is configured to perform the PCR test within six hours of the blood sample being obtained.

In some embodiments, the techniques described herein relate to a method for identifying pathogens and their antimicrobial resistance genes in a subject suspected of having an infectious disease, including: obtaining a peripheral blood sample from the subject, extracting RNA from the peripheral blood sample, performing high-throughput RNA sequencing on the extracted RNA to generate RNA sequencing data, filtering the RNA sequencing data to remove sequences aligning to the human genome, aligning the filtered RNA sequencing data to one or more curated antimicrobial resistance gene databases and one or more curated pathogen genome databases, designing specific molecular primers based on the aligned RNA sequencing data, wherein the molecular primers target sequences specific to the one or more pathogenic microorganisms and the one or more antimicrobial resistance genes, and identifying, using the specific molecular primers in a polymerase chain reaction (PCR) assay, the presence and identity of one or more pathogenic microorganisms and one or more antimicrobial resistance genes associated with the one or more pathogenic microorganisms in the peripheral blood sample. The techniques described herein relate to a method, wherein the subject is a patient diagnosed with sepsis or suspected of having sepsis based on clinical signs and symptoms.

In some embodiments, the techniques described herein relate to a method, wherein the subject is a patient with a confirmed infectious disease or suspected of having an infectious disease based on clinical signs and symptoms. The techniques described herein relate to a method, wherein the RNA sequencing data that does not align to the human genome is further filtered to remove low-quality sequences and contaminant sequences before aligning to the one or more antimicrobial resistance gene databases and the one or more pathogen genome databases.

In some embodiments, the techniques described herein relate to a method, wherein the specific molecular primers are used in a quantitative real-time PCR (qRT-PCR) assay to simultaneously identify and quantify the one or more pathogenic microorganisms and the one or more antimicrobial resistance genes in the peripheral blood sample. The techniques described herein relate to a method, wherein the PCR assay is performed using a multiplex PCR platform capable of detecting multiple targets in a single reaction.

In some embodiments, the techniques described herein relate to a method, wherein the method is performed by a trained healthcare professional in a clinical laboratory setting equipped with high-throughput sequencing and PCR facilities. The techniques described herein relate to a method, wherein the method can diagnose the presence and identity of the one or more pathogenic microorganisms and the one or more antimicrobial resistance genes within 24 hours of obtaining the peripheral blood sample, which is significantly faster than standard culture-based diagnostic methods that typically require 48-72 hours.

In some embodiments, the techniques described herein relate to a method, wherein rapidly identifying the presence and identity of the one or more pathogenic microorganisms and the one or more antimicrobial resistance genes in the patient with sepsis enables timely and appropriate selection of targeted antimicrobial therapy, which improves clinical outcomes and reduces mortality in sepsis. The techniques described herein relate to a method, wherein identifying the specific one or more antimicrobial resistance genes associated with the one or more pathogenic microorganisms enables the selection of effective and targeted antimicrobial treatments, while avoiding the unnecessary use of broad-spectrum antibiotics, thereby facilitating antimicrobial stewardship and reducing the spread of antimicrobial resistance.

In some embodiments, the techniques described herein relate to a method for diagnosing and treating infectious diseases in a subject, including: obtaining a biological sample from the subject, extracting RNA from the biological sample, performing high-throughput RNA sequencing on the extracted RNA to obtain RNA sequencing data, identifying, based on the RNA sequencing data, one or more pathogenic microorganisms and one or more antimicrobial resistance genes associated with the one or more pathogenic microorganisms by: aligning the RNA sequencing data to one or more curated antimicrobial resistance gene databases and one or more curated pathogen genome databases, and designing specific molecular primers based on the aligned RNA sequencing data, wherein the molecular primers target sequences specific to the one or more pathogenic microorganisms and the one or more antimicrobial resistance genes, determining an antimicrobial susceptibility profile of the one or more pathogenic microorganisms based on the identified one or more antimicrobial resistance genes, and selecting a targeted antimicrobial treatment for the subject based on the determined antimicrobial susceptibility profile. The techniques described herein relate to a method, wherein the biological sample is peripheral blood, and the RNA sequencing data is obtained by performing high-throughput RNA sequencing on RNA extracted from the peripheral blood sample.

In some embodiments, the techniques described herein relate to a method, wherein identifying the one or more pathogenic microorganisms and the one or more antimicrobial resistance genes further includes: filtering the RNA sequencing data to remove sequences aligning to the human genome and other contaminant sequences, and using the specific molecular primers in a quantitative real-time PCR (qRT-PCR) assay to confirm the presence and identity of the one or more pathogenic microorganisms and the one or more antimicrobial resistance genes in the biological sample. The techniques described herein relate to a method, wherein the targeted antimicrobial treatment is selected to specifically target the identified one or more pathogenic microorganisms while minimizing the use of broad-spectrum antibiotics, based on the determined antimicrobial susceptibility profile.

In some embodiments, the techniques described herein relate to a method, wherein the method is performed by a multidisciplinary team of healthcare professionals, including clinicians, microbiologists, and bioinformaticians, in ahospital setting equipped with high-throughput sequencing and PCR facilities. The techniques described herein relate to a method, wherein the method can diagnose the infectious disease and determine the appropriate targeted antimicrobial treatment within 24-48 hours of obtaining the biological sample, which is significantly faster than standard culture-based diagnostic methods that typically require 48-72 hours for pathogen identification and antimicrobial susceptibility testing.

In some embodiments, the techniques described herein relate to a method, wherein the infectious disease is sepsis, and the rapid diagnosis and targeted antimicrobial treatment enabled by the method significantly improves clinical outcomes and reduces mortality in patients with sepsis compared to standard care. The techniques described herein relate to a method, wherein the qRT-PCR assay is performed using a multiplex PCR platform capable of simultaneously detecting and quantifying multiple pathogenic microorganisms and antimicrobial resistance genes in a single reaction, thereby increasing the efficiency and accuracy of the diagnostic process.

In some embodiments, the techniques described herein relate to a method, wherein selecting the targeted antimicrobial treatment based on the determined antimicrobial susceptibility profile facilitates antimicrobial stewardship by ensuring the appropriate use of antibiotics and reducing the unnecessary use of broad-spectrum antibiotics, thereby minimizing the spread of antimicrobial resistance. The techniques described herein relate to a method, wherein the biological sample includes one or more of: peripheral blood, sputum, bronchoalveolar lavage fluid, cerebrospinal fluid, urine, wound swabs, or tissue biopsies, depending on the suspected site of infection and the clinical presentation of the subject.

List of Embodiments

Specific compositions and methods of the invention have been described. The detailed description in this specification is illustrative and not restrictive or exhaustive. The detailed description is not intended to limit the disclosure to the precise form disclosed. Other equivalents and modifications besides those already described are possible without departing from the inventive concepts described in this specification, as persons skilled in the biomedical art recognizes. When the specification or claims recite method steps or functions in an order, alternative embodiments may perform the functions in a different order or substantially concurrently. The inventive subject matter should not be restricted except in the spirit of the disclosure.

When interpreting the disclosure, all terms should be interpreted in the broadest possible manner consistent with the context. Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as commonly understood by persons of ordinary skill in the biomedical art to which this invention belongs. This invention is not limited to the particular methodology, protocols, reagents, and the like described in this specification and can vary in practice. The terminology used in this specification is not intended to limit the scope of the invention, which is defined solely by the claims.

When a range of values is provided, each intervening value, to the tenth of the unit of the lower limit, unless the context dictates otherwise, between the upper and lower limit of that range and any other stated or intervening value in that range of values.

Some embodiments of the technology described can be defined according to the following numbered paragraphs.

Any of the embodiments, claims, or aspects disclosed herein can be combined or inter-combined with any of the discussion points (numbers) below. The technology can be discussed in an overview by the following embodiments.

1. A direct from blood, without the need for culture, reverse transcriptase polymerase chain reaction (RT-qPCR) test for bacteria causing bacteremia, based on the RNA identified in patients with bacteremia caused by these bacteria, optionally selected from the group consisting of Staphylococcus aureus, Pseudomonas æruginosa, and Hæmophilus influenzae.

2. A method to validate these RT-qPCR tests in samples from patients with and without bacteremia.

3. A direct from blood, without the need for culture, reverse transcriptase polymerase chain reaction (RT-qPCR) test for bacteria causing pneumonia, based on the RNA identified in patients with pneumonia caused by these bacteria, optionally selected from the group consisting of Staphylococcus aureus, Pseudomonas æruginosa, and Hæmophilus influenzae.

4. A method to validate these RT-qPCR tests in samples from patients with and without pneumonia.

5. An RT-PCR for the most common resistance genes expressed that would influence treatment of bacteria, optionally selected from the group consisting of Staphylococcus aureus, Pseudomonas æruginosa, and Hæmophilus influenzae, using the RNA from patients with infections.

6. A method to validate these PCR tests for resistance genes in samples from patients with and without infections.

7. A method for rapidly identifying pathogens and their antimicrobial resistance genes in a subject suspected of having an infectious disease, comprising: obtaining a peripheral blood sample from the subject, extracting total RNA from the peripheral blood sample using a commercially available RNA extraction kit optimized for blood samples, assessing the quality and quantity of the extracted RNA using a bioanalyzer or equivalent technology, preparing a sequencing library from the extracted RNA using a high-throughput RNA sequencing library preparation kit, performing high-throughput paired-end RNA sequencing on the prepared sequencing library using a next-generation sequencing platform to generate at least 20 million paired-end reads of at least 150 base pairs in length, quality filtering the generated RNA sequencing reads to remove low-quality reads, adapter sequences, and reads aligning to the human genome using a combination of commercially available and custom-built bioinformatics tools, aligning the quality-filtered RNA sequencing reads to one or more curated antimicrobial resistance gene databases and one or more curated pathogen genome databases using a high-performance sequence alignment tool, analyzing the aligned sequencing data to identify the presence and relative abundance of one or more pathogenic microorganisms and one or more associated antimicrobial resistance genes, designing specific molecular primers and probes based on the identified pathogenic microorganisms and antimicrobial resistance genes, wherein the primers and probes target sequences specific to the identified pathogenic microorganisms and antimicrobial resistance genes, and validating the presence and identity of the one or more pathogenic microorganisms and one or more antimicrobial resistance genes in the peripheral blood sample using the designed primers and probes in a multiplex quantitative real-time PCR (qRT-PCR) assay.

8. The method of embodiment 7, wherein the subject is a patient diagnosed with sepsis based on the presence of two or more systemic inflammatory response syndrome (SIRS) criteria and a suspected or confirmed infection, or a patient suspected of having sepsis based on the presence of one or more SIRS criteria and a suspected infection.

9. The method of embodiment 7, wherein the subject is a patient with a confirmed infectious disease based on clinical signs and symptoms, laboratory findings, and/or diagnostic imaging, or a patient suspected of having an infectious disease based on clinical signs and symptoms and/or preliminary laboratory findings.

10. The method of embodiment 7, wherein the RNA sequencing data that does not align to the human genome is further filtered to remove low-complexity sequences, repetitive sequences, and sequences aligning to known contaminants, such as ribosomal RNA, mitochondrial RNA, and phiX control DNA, before aligning to the one or more antimicrobial resistance gene databases and the one or more pathogen genome

11. The method of embodiment 7, wherein the specific molecular primers and probes are designed using a combination of commercially available primer design software and manual curation based on the aligned sequencing data, and wherein the primers and probes are optimized for use in a multiplex qRT-PCR assay, with each primer and probe set targeting a specific pathogenic microorganism or antimicrobial resistance gene.

12. The method of embodiment 7, wherein the multiplex qRT-PCR assay is performed using a commercially available qRT-PCR platform and kit, with each reaction containing multiple primer and probe sets targeting different pathogenic microorganisms and antimicrobial resistance genes, and wherein the assay is validated using positive and negative control samples, as well as spiked-in synthetic DNA or RNA standards.

13. The method of embodiment 7, wherein the method is performed by a trained healthcare professional, such as a clinical microbiologist or molecular diagnostics technician, in a clinical laboratory setting equipped with high-throughput sequencing and qRT-PCR facilities, and wherein the results are interpreted by a qualified healthcare professional, such as a clinical microbiologist or infectious disease specialist, in the context of the patient's clinical presentation and other diagnostic findings.

14. The method of embodiment 7, wherein the entire process from sample collection to result reporting can be completed within 12-24 hours, which is significantly faster than standard culture-based diagnostic methods that typically require 48-72 hours for pathogen identification and antimicrobial susceptibility testing, and wherein the rapid turnaround time enables early initiation of targeted antimicrobial therapy, which improves clinical outcomes and reduces mortality in patients with severe infections.

15. The method of embodiment 7, wherein rapidly identifying the presence and relative abundance of one or more pathogenic microorganisms and one or more associated antimicrobial resistance genes in the patient with sepsis enables the selection of an appropriate targeted antimicrobial therapy based on the identified pathogens and their predicted antimicrobial susceptibility profile, which improves clinical outcomes and reduces mortality compared to empiric broad-spectrum antimicrobial therapy.

16. The method of embodiment 7, wherein identifying the specific antimicrobial resistance genes associated with the identified pathogenic microorganisms enables the prediction of potential resistance to specific classes of antimicrobial agents, which informs the selection of effective targeted antimicrobial treatments, while avoiding the unnecessary use of broad-spectrum antibiotics that may be ineffective or contribute to the development and spread of antimicrobial resistance.

17. A method for rapidly diagnosing and treating infectious diseases in a subject by combining high-throughput RNA sequencing with multiplex quantitative real-time PCR (qRT-PCR), comprising: obtaining a biological sample from the subject, wherein the biological sample is selected based on the suspected site of infection and may include peripheral blood, sputum, bronchoalveolar lavage fluid, cerebrospinal fluid, urine, wound swabs, or tissue biopsies, extracting total RNA from the biological sample using a commercially available RNA extraction kit optimized for the specific sample type, assessing the quality and quantity of the extracted RNA using a bioanalyzer or equivalent technology, preparing a sequencing library from the extracted RNA using a high-throughput RNA sequencing library preparation kit, performing high-throughput paired-end RNA sequencing on the prepared sequencing library using a next-generation sequencing platform to generate at least 20 million paired-end reads of at least 150 base pairs in length, quality filtering the generated RNA sequencing reads to remove low-quality reads, adapter sequences, reads aligning to the human genome, and other contaminant sequences using a combination of commercially available and custom-built bioinformatics tools, aligning the quality-filtered RNA sequencing reads to one or more curated antimicrobial resistance gene databases and one or more curated pathogen genome databases using a high-performance sequence alignment tool, analyzing the aligned sequencing data to identify the presence and relative abundance of one or more pathogenic microorganisms and one or more associated antimicrobial resistance genes, designing specific molecular primers and probes based on the identified pathogenic microorganisms and antimicrobial resistance genes, wherein the primers and probes target sequences specific to the identified pathogenic microorganisms and antimicrobial resistance genes, validating the presence and identity of the one or more pathogenic microorganisms and one or more antimicrobial resistance genes in the biological sample using the designed primers and probes in a multiplex qRT-PCR assay, determining the antimicrobial susceptibility profile of the identified pathogenic microorganisms based on the presence and relative abundance of specific antimicrobial resistance genes, and selecting a targeted antimicrobial treatment regimen for the subject based on the identified pathogenic microorganisms, their relative abundance, and their predicted antimicrobial susceptibility profile.

18. The method of embodiment 17, wherein the biological sample is peripheral blood, and the RNA sequencing and qRT-PCR assays are performed on RNA extracted from whole blood or isolated blood components, such as plasma, serum, or leukocytes, depending on the suspected pathogen and the optimal sample type for its detection.

19. The method of embodiment 17, wherein identifying the one or more pathogenic microorganisms and one or more associated antimicrobial resistance genes further comprises: taxonomically classifying the aligned sequencing reads at the species or strain level using a metagenomics classification tool, assembling the aligned sequencing reads into contiguous sequences (contigs) using a de novo assembly tool, comparing the assembled contigs to reference genome sequences of known pathogenic microorganisms and antimicrobial resistance genes using a sequence alignment tool, and estimating the relative abundance of each identified pathogenic microorganism and antimicrobial resistance gene based on the number of aligned sequencing reads and the coverage of the assembled contigs.

20. The method of embodiment 17, wherein the targeted antimicrobial treatment regimen is selected based on the identity and relative abundance of the pathogenic microorganisms, their predicted antimicrobial susceptibility profile, the site and severity of the infection, the subject's clinical status and comorbidities, and the pharmacokinetic and pharmacodynamic properties of the available antimicrobial agents, with the goal of maximizing efficacy and minimizing toxicity and the development of antimicrobial resistance.

21. The method of embodiment 17, wherein the method is performed by a multidisciplinary team of healthcare professionals with expertise in clinical microbiology, molecular diagnostics, bioinformatics, and infectious diseases, who work together to optimize the sample collection, processing, sequencing, data analysis, and interpretation of results, and to communicate the findings and treatment recommendations to the treating clinicians in a timely and effective manner.

22. The method of embodiment 17, wherein the entire process from sample collection to result reporting and treatment selection can be completed within 8-12 hours in urgent cases, such as sepsis or meningitis, and within 24-48 hours in less urgent cases, which is significantly faster than standard culture-based diagnostic methods that typically require 48-72 hours for pathogen identification and antimicrobial susceptibility testing, and wherein the rapid and accurate diagnosis and treatment enabled by this method improves clinical outcomes, reduces complications and mortality, and shortens hospital stays compared to empiric or delayed treatment based on standard diagnostic methods.

23. The method of embodiment 17, wherein the method is particularly useful for diagnosing and treating sepsis, which is a life-threatening condition characterized by a dysregulated host response to infection that can lead to organ dysfunction and death, and wherein the rapid identification of the causative pathogens and their antimicrobial susceptibility profile enables the early initiation of targeted antimicrobial therapy, which is critical for improving survival and reducing morbidity in patients with sepsis.

24. The method of embodiment 23, wherein the multiplex qRT-PCR assay is designed to detect and quantify up to 50 different pathogenic microorganisms and antimicrobial resistance genes in a single reaction, using a combination of species-specific and genus-specific primers and probes that are labeled with distinct fluorescent dyes, and wherein the assay is optimized to minimize cross-reactivity and background noise, and to provide a limit of detection of 10-100 copies per reaction for each target

25. The method of embodiment 24, wherein the selection of the targeted antimicrobial treatment regimen is guided by evidence-based clinical practice guidelines and institutional antimicrobial stewardship protocols, which take into account the local epidemiology of infectious diseases and antimicrobial resistance patterns, as well as the patient's clinical factors and preferences, and wherein the use of targeted antimicrobial therapy based on rapid molecular diagnostic results was shown to improve clinical outcomes, reduce healthcare costs, and limit the emergence and spread of antimicrobial resistance compared to empiric broad-spectrum therapy.

26. The method of embodiment 17, wherein the method can be adapted to different healthcare settings and patient populations by modifying the sample collection and processing procedures, the sequencing and qRT-PCR assay parameters, and the data analysis and interpretation algorithms based on the available resources, the suspected pathogens, and the clinical needs, and wherein the method can be integrated into existing diagnostic workflows and electronic health record systems to facilitate the timely and accurate diagnosis and treatment of infectious diseases across the healthcare continuum. Machine learning, particularly graph-based models, has emerged as a promising approach to tackle these challenges. Graph neural networks (GNNs) and graph convolutional networks (GCNs) are types of machine learning models that excel in capturing the relationships and interactions between different entities, making them well-suited for analyzing biological data where genes and their interactions can be naturally represented as graphs. By integrating additional layers of information, such as surprisal analysis data, these models can potentially uncover hidden patterns and interactions that are not apparent from scRNA-seq data alone. This integration aims to enhance the predictive power and interpretability of the models, thereby facilitating the identification of therapeutic targets and advancing understanding of complex biological systems.

Specific compositions and methods of the invention have been described. The detailed description in this specification is illustrative and not restrictive or exhaustive. The detailed description does not intend to limit the disclosure to the precise form described. Other equivalents and modifications besides those already described are possible without departing from the inventive concepts described in this specification, as persons having ordinary skill in the biomedical art will recognize. When the specification or claims recite method steps or functions in an order, alternative embodiments may perform the functions in a different order or concurrently. The inventive subject matter should not be restricted except in the spirit of the disclosure.

This invention is not limited to the particular methods, protocols, and reagents described in this specification and can vary in practice. The invention is defined only by the claims.

When a range of values is provided, each intervening value, to the tenth of the unit of the lower limit, unless the context dictates otherwise, between the upper and lower limit of that range and any other stated or intervening value in that range of values.

All patents and publications cited throughout this specification are incorporated by reference to disclose and describe the materials and methods that might be used with the technologies described in this specification. The publications discussed are provided for their disclosure before the filing date. They should not be construed as an admission that the inventors may not antedate such disclosure under prior invention or for any other reason. If there is an apparent discrepancy between a prior patent or publication and the description provided in this specification, the specification (including any definitions) and claims shall control.

The statements about the date or contents of these documents are based on the information available to the applicants. These statements constitute no admission to the correctness of the dates or contents of these documents. The publication dates provided in this specification may differ from the actual publication dates. If there is an apparent discrepancy between a publication date provided in this specification and the actual publication date supplied by the publisher, the actual publication date shall control.

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All patents and publications cited throughout this specification are expressly incorporated by reference to disclose and describe the materials and methods that might be used with the technologies described in this specification. The publications discussed are provided solely for their disclosure before the filing date. They should not be construed as an admission that the inventors may not antedate such disclosure under prior invention or for any other reason. If there is an apparent discrepancy between a previous patent or publication and the description provided in this specification, the specification, including any definitions, and claims shall control. All statements as to the date or representation as to the contents of these documents are based on the information available to the applicants and constitute no admission as to the correctness of the dates or contents of these documents. The dates of publication provided in this specification may differ from the actual publication dates. If there is an apparent discrepancy between a publication date provided in this specification and the actual publication date supplied by the publisher, the actual publication date shall control.